US 20080094420 A1
A system and method for automatic creation of digital image albums. A Page Creator Module utilizes a genetic engine and a layout evaluation module. The genetic engine evolves a group of images to a plurality of album pages, based on certain layout criteria. The evaluation module calculates layout criteria and compares them with user preferences. When an acceptable image/page layout has been generated, the image/page assignments are transferred to an Image Placement Module. The Image Placement Module utilizes a second genetic engine, which evolves various criteria to generate page layouts genetic structures. These structures define the location, scale, and rotation of images placed on a given page. A layout evaluation module calculates and compares these layouts with certain other preferences and page requirements. When a suitable layout has been generated, a final album output is generated, which may be displayed, printed, or otherwise transferred for subsequent utilization.
1. An automated album layout method responsive to a set of inputs containing digital images, graphics, and other 2-dimensional objects, comprising the steps of:
receiving pluralities of user album preferences and album preference importance values, said user album preferences indicating parameter values including at least one of balance, emphasis, chronology, and unity, each said album preference importance value indicating a weighting of a corresponding one of said user album preferences relative to the other said user album preferences,
generating a fitness function based upon said user album preferences and said album preference importance values;
evaluating a grouping of the image objects for distribution into a number of album pages using a genetic algorithm, according to said fitness function;
assigning each said image object to a page based on said evaluating;
displaying said page for user viewing, and
refining the distribution based on further user action.
2. An automated layout and presentation method responsive to a set of inputs containing digital images, graphics, and other two-dimensional objects, comprising the steps of:
receiving pluralities of user page preferences and page preference importance values, said user page preferences indicating parameter values including at least one of white space, overlap, rotation, spatial balance, rotational balance, border symmetry, and emphasis, each said page preference importance value indicating a weighting of a corresponding one of said user page preferences relative to the other said user page preferences,
generating a fitness function based upon said user page preferences and said page preference importance values;
evaluating the ‘x’ and ‘y’ position coordinates, scale, and rotation of each of the input images objects within a page using a genetic algorithm, according to said fitness function;
creating a page layout based on said evaluating;
displaying said page layout for user viewing;
refining said page layout based on further user action, and
formatting the page layout printing.
3. A system for assigning a plurality of images to album pages, comprising:
means for receiving a plurality of different user album preferences and a plurality of album preference importance values, each said album preference importance value indicating a weighting of a corresponding one of said user album preferences relative to the other said user album preferences,
means for specifying an initial set of page assignments of said images to a genetic population;
a genetic engine operable to evolve said genetic population to produce a present set of image page assignments;
a page evaluation module operable to generate an album fitness function using said user album preferences and album preference importance values and to test said present set of image page assignments according to said album fitness function to determine an album score, and
means for outputting said present set of image page assignments if said album score meets an album threshold value.
4. A system for arranging a plurality of images on an album page, comprising:
means for receiving a plurality of different user page preferences and a plurality of page preference importance values, each said page preference importance value indicating a weighting of a corresponding one of said user page preferences relative to the other said user page preferences,
means for specifying an initial set of image placement parameters of the images to a genetic population;
a genetic engine operable to evolve said genetic population to produce a present set of image placement parameters;
a layout evaluation module operable to generate a page fitness function using said user page preferences and page preference importance values and to test said present set of image placement parameters with a said page fitness function to determine a page score; and
a means for outputting said present set of image placement parameters if said page score meets a page threshold value.
5. A method of assigning a plurality of images to album pages, comprising the steps of:
receiving a plurality of different user album preferences and a plurality of album preference importance values, each said album preference importance value indicating a weighting of a corresponding one of said user album preferences relative to the other said user album preferences,
specifying an initial set of page assignments of the images to a genetic population;
evolving said genetic population to produce a present set of image page assignments;
generating an album fitness function using said user album preferences and album preference importance values;
testing said present set of image page assignments according to said album fitness function to determine an album score, and
outputting said present set of image page assignments if said album score meets an album threshold value.
6. A method of arranging a plurality of images on an album page, comprising the steps of:
receiving a plurality of different user page preferences and a plurality of page preference importance values, each said page preference importance value indicating a weighting of a corresponding one of said user page preferences relative to the other said user page preferences,
specifying an initial set of image placement parameters of the images to a genetic population;
evolving said genetic population to produce a present set of image placement parameters;
generating a page fitness function based upon said user page preferences and said page preference importance values;
testing said present set of image placement parameters with said page fitness function to determine a page score; and
outputting said image placement parameters if said page score meets a page threshold value.
7. The method of
repeating said evolving and testing steps if said album score fails to meet said album threshold value.
8. The method of
9. The method of
This application is a continuation of patent application Ser. No. 09/750,858, filed Dec. 29, 2000, entitled, “SYSTEM AND METHOD FOR AUTOMATIC LAYOUT OF IMAGES IN DIGITAL ALBUMS”, the entire disclosure of which is hereby incorporated herein by reference.
The invention relates generally to software. More particularly, the present invention relates to software for creating digital photo albums.
Modern photography is moving into the digital age. Even though a large part of the market for photography still utilizes conventional film and processing, the ability to obtain digital files from conventional film is rapidly adapting the conventional film market into the digital image arena. In addition, with the broad acceptance of digital cameras, as well as broad utilization of digital images in Internet applications, the volume of commercial and consumer produced digital image files has increased dramatically in recent years. Suppliers now routinely deliver digital image files to users. Such files may be delivered on storage media, like photo CD's and so forth, or may be delivered via the Internet or through e-mail. The provision of digital images in an organized format, including the preparation of digital image photo albums, is now available in the marketplace, as consumers desire to receive and present their images in a most favorable light. However, there has yet to be introduced an automated system that presents digital images in sophisticated creatively composed fashions.
There presently exist several software applications, which assist the user in manual creation of digital photo albums. In general, these applications provide the user with a straightforward means of accomplishing the basic task of image organization and page layout, so called ‘albuming’. The drawback with such applications is that they require a large amount of user interaction, which does not yield much improvement over the conventional, paper based albuming techniques of the past. Therefore, the task is less likely to be completed due to the significant amount of effort required to accomplish it.
Automated albuming systems that offer limited automated layout functions are known in the art. Eastman Kodak Company has developed digital graphic album applications, such as the Kodak Picture Page software, which allow a user to organize digital graphics images into album pages. Typically, users organize images by dates and times, places, subject and so forth. Such software allows the user to annotate the images by entering text, or other data, describing the image. One common approach to automated layout is the use of templates. In this approach, predefined layouts with empty areas are filled in with user images. Thus, the size, placement, rotation, and framing of the images on the page are predetermined. A user or system merely needs to specify which images should be placed in which empty area to complete an album page. This approach is also widely used by many graphic layout design tools (such as Quark) and by Kodak® PicturePage® Software. Templates provide a straightforward and working solution to the layout problem. However, this solution is somewhat limiting as the choice of layouts is bounded by the collection of available templates. Variation in page layout design can only be achieved by the addition of new templates.
The real challenge in automatic generation of page layouts is allowing a level of creativity in the layouts produced. With the introduction of scrapbooking as a social activity, there has been a recent resurgence of interest in capturing memories and telling stories using scrapbooks. The albums produced by people in these communities can range from extremely basic, where images are laid out using a fixed grid or template, to extremely complex, where images are seemingly scattered randomly on a page. In such layouts, it is very difficult to define, in an objective fashion, what the layout approach is.
Consequently, a need exists in the art for an automated system and/or method of organizing and generating album and page layouts of digital images.
The need in the art is addressed by the systems and methods of the present invention. The Automatic Albuming System taught by the present invention is unique, in part, because it produces album pages automatically with minimal input from the user. In an illustrative embodiment, a digital image album layout system is taught. The system comprises a page creator module that has a first genetic engine operable to execute genetic evolution calculations on a first genetic population of image criteria. The page creator module also has a page evaluation module that is operable to test the first genetic population for fitness to album preference criteria. The system also has an image placement module with a second genetic engine that is operable to execute genetic evolution calculations on a second genetic population of page layout criteria. The image placement module also has a layout evaluation module that is operable to test the second genetic population for fitness to page preference criteria.
The present invention teaches an automated album layout method. The method involves the use of a set of inputs including digital images, graphics, and other 2-dimensional objects. The inventive method comprises the steps of evaluating a grouping of the image objects for distribution into a number of album pages according to a fitness function's parameters of a genetic engine and, assigning each image object to a page based on user preferences, including at least one of: balance, emphasis, chronology, and unity. Then, the page is displayed for user viewing and distribution refining the based on further user action.
The present invention also teaches an automated layout and presentation method responsive to a set of inputs containing digital images, graphics, and other two-dimensional objects. This method comprises the steps of evaluating the ‘x’ and ‘y’ position coordinates, scale and rotation of each of the input images objects within a page according to fitness function parameters in a genetic engine. Then, a page layout is created based on user preferences including at least one of: white space, overlap, rotation, spatial balance, rotational balance, border symmetry, and emphasis. Then, the page layout is displayed for user viewing, and refining the page layout based on further user action. Finally, the page layout is formatted for printing.
In an illustrative embodiment, a system for assigning images to album pages is taught. The system includes a mechanism for specifying an initial set of image page assignments to a genetic population and a genetic engine operable to evolve the genetic population to produce a present set of image page assignments. The system also includes a page evaluation module operable to test the present set of image page assignments according to an album fitness function to determine an album score and a mechanism for outputting the present set of image page assignments if the album score meets an album threshold value.
In another illustrative embodiment, a system for arranging images on an album page is taught. This system includes a mechanism for specifying an initial set of image placement parameters to a genetic population and a genetic engine operable to evolve the genetic population to produce a present set of image placement parameters. The system also includes a layout evaluation module, operable to test the present set of image placement parameters with a page fitness function to determine a page score, and a mechanism for outputting the image placement parameters if the page score meets a page threshold value.
While the present invention is described herein with reference to illustrative embodiments for particular applications, it should be understood that the invention is not limited thereto. Those having ordinary skill in the art and access to the teachings provided herein will recognize additional modifications, applications, and embodiments within the scope thereof and additional fields in which the present invention would be of significant utility.
The principle objective of albuming automation is to automate the album production process using various image science algorithms and techniques. The final step of this automated process is to layout images within an album in a manner pleasing to the user.
The present invention teaches an innovative and flexible system for automatic album page layout that makes advantageous use of genetic algorithms. The system is divided into two modules, a Page Creator Module which is responsible for distributing images amongst various album pages and an Image Placement Module which positions images on individual pages. Each module incorporates basic graphic design principles such as balance, emphasis, rhythm, and unity, in determining fitness for page layout solutions.
A complete albuming automation system utilizes various image science algorithms and techniques including advanced event clustering, dud detection, image appeal and automatic page layout. In an illustrative embodiment, the emphasis of such a system is for a “DAFY” (Do-it-All-For-You) like product, where the user inputs a collection of images and the system produces an album (a collection of images) with minimal input from the user. It will be understood by those of ordinary skill in the art, that the term ‘images’ encompasses a much broader scope than the conventional photograph, even though the album concept stems from the traditional photographic album. In the modern digital world, images include computer generated graphics, bitmaps, photographs, computer altered photographs, video still frames, scanned images, various forms or artwork, text, background materials, and even video clips, animation, and computer generated time variant materials.
An overall functional diagram of an illustrative embodiment Albuming Automation System (‘AAS’) 2 is depicted in
The core processing of the AAS 2 includes several functions 26 that discriminate images and information for subsequent page layout. These include clustering of images by event 28, detection of dud images 30, detection of duplicate images 32, recognition of facial features and certain other objects 34, audio to text conversion 36, and video summarization 38. The reduced and refined image information is then coupled to a second group of core process functions 40 that further refine the image content information. These functions include selection of the best image per page 42, automatic image cropping 44, association of particular images with ancillary content 46, association with event description information 48, and association with caption and annotation information 50. The refined image information is coupled to the automatic page layout process 52, which will be more fully described hereinafter. Page layout data is coupled to an output format module 54 that further organizes the output from the automatic page layout module 52. Finally, the output of the AAS 2 is produced at module 56 in the form of single page images 58, entire albums of images 60, Picture CD media, or other photo delivery media as are understood by those of ordinary skill in the art.
The present invention teaches a flexible system for generation of album page layouts. The system makes use of genetic algorithms, a class of search and optimization algorithms that are based on the concepts of biological evolution. For a more detailed reference to genetic algorithms, see; J. Holland, Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, 1975, and, D. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989, the contents of which are hereby incorporated by reference thereto. The system is comprised of two major modules, the first that distributes images amongst a set of album pages, and the second that positions the images on each individual page. These modules are called the Page Creator Module and the Image Placement Module respectively. Each module takes a genetic approach to its task.
The overall function of the Page Layout System 124 is straightforward. Given a set of images to be placed in an album, a page layout algorithm must distribute the images amongst a set of pages and then layout the images on each individual page. Working within the framework of the AAS, the following information is available to the page layout system 124 on an image by image basis:
The page layout system 124 performs two separate, yet equally important tasks. Page creation 126, given a set of images, the system distributes these images amongst a set of album pages, such that each image is assigned a page upon which the image will appear. And, image placement 132, once the images have been assigned to pages, each individual page is laid out by positioning the images assigned to it. Therefore, for each image, placement, rotation, and scaling of the image on the page are assigned.
With regard to album layout according to the present invention, an important advantage is that the Automated Albumimg System produces albums that more closely resemble scrapbooks as opposed to a simple collection of pictures. Achieving this goal in an automated fashion is a significant accomplishment because the means by which creative scrapbookers generate page layouts for their albums is usually not easily expressed in an objective fashion. The creation of a scrapbook is primarily a subjective and artistic task. Few, if any, concrete rules exist in the scrapbook generation process, and those that do, tend to be individual based on personal preferences. The subjective nature of creative page layout poses a real challenge to any page layout system. In particular, template based layout approaches are somewhat limiting since the range of possibilities for a page layout are bounded by the collection of available templates.
The present invention employs a novel approach to page layout by employing genetic algorithms, which are a class of adaptive methods that can be used to solve search and optimization problems involving large search spaces. The search is performed using a simulated evolution (survival of the fittest). These algorithms maintain and manipulate “generations” of potential solutions or “populations”. With each generation, the best solutions (as determined by a problem specific fitness function) are genetically manipulated to form the solution set for the following generation. As in real evolution, solutions can be combined (via mating/crossover) or undergo random mutation. In addition, inferior solutions can, by chance, survive from generation to generation.
The genetic algorithm search process is performed in an iterative manner as illustrated in
When applying a genetic algorithm to a given problem, three major tasks must be performed:
1) Coding—Genetic algorithms maintain populations of problem solutions. During implementation, these solutions are represented by some sort of data structure. The data structure used by a genetic algorithm is known as a genome. In the coding task, a data structure is chosen to represent the genome for the problem space and a mapping from the data structure fields to the problem domain is established. Common genome data structures used in genetic algorithms includes lists, arrays, and trees.
2) Definition of Genetic Operators—New solutions are created via crossover and mutation of individuals from previous generations. Given a particular genome structure, the means for performing these operations must be defined. During crossover, one or more children solutions are derived from two or more parents. With mutation, new individuals are generated by mutation of a single solution. There are standard crossover and mutation operators available for genomes encoded using commonly used data structures like lists, arrays, and trees.
3) Fitness—The most challenging and application specific task in applying genetic algorithms to a problem domain is in the definition of a fitness function. The fitness function is responsible for judging individual solutions and returning a score based on its evaluation. In essence, the fitness function defines the difference between a good solution to a problem and a bad one. Much care must be taken in defining the fitness function, as the genetic algorithm will converge on solutions deemed “fit” by this function.
An impetus to using genetic algorithms for page layout came from an application for creating artistic textures. This source was Karl Sims who uses genetic algorithms for creating 2D textures as articulated in the reference; Karl Sims, “Artificial Evolution for Computer Graphics”, Proceedings of SIGGRAPH 91, pp 319-328, the contents of which are hereby incorporated by reference thereto. The motivations behind his work was mostly artistic whereby the artist directly determined the fitness of each solution by visual inspection. The system allowed for random exploration of the texture space with solutions converging based on the likes and dislikes of the artist.
Page layout has also been found to be more of an artistic task rather than a mechanical one. Genetic algorithms are appropriate for such artistic tasks since, unlike other more brute force algorithms, the genetic algorithm does not attempt to mimic or model any particular process by which solutions are created. Instead, solutions are generated randomly and are evaluated after the fact. This is analogous to the way creative scrapbookers approach the layout problem. While scrapbookers can't usually explain the process by which they generated their page layouts, they certainly know a good layout when they see one.
The layout problem has a multidimensional problem space. Considering the image distribution task discussed herein before, there are at least four parameters that must determined for each image that is to be positioned on a page. These are the ‘x’ and ‘y’ positions, the rotation angle of each image, and the size scaling of each image. Multiplying that by the number of images to be placed results in a solution space ranging from four dimensions, in the case of a single image to be placed, to as high as forty dimensions, in the case of ten images to be placed. Genetic algorithms have been proven successful for problems with similarly large dimensional solution spaces, hence they are suitable for automatic albuming system applications.
The most important implementation task involves the definition of the fitness function. In defining the fitness for page layout, an approach whereby the fitness is determined directly by the user's visual evaluation would be desirable since the user's subjective interpretation would naturally fit the user's expectations of artistic qualities. The AAS, however, is not principally designed for this type of interaction, as one of the goals of the AAS is to minimize the user input once the images to be placed in the album have been input to the system. This does not, however, limit the application of the present invention page layout system in a more interactive environment.
Due to the subjective nature of album evaluation, some indication of the kind of layouts to be produced is required. Page layout is one of the major tasks of those skilled in the art of graphic design. In defining a means for a user to specify album layout preference, the present invention may rely on the principles of graphic design. Upon surveying a number of graphic design texts, including: Amy Arntson, Graphic Design Basics, 3rd Ed., Harcourt Brace College Publishers, Fort Worth, 1998; Bryan L. Peterson, Using Design Basics To Get Creative Results, Northern Lights Books, Cincinnati, Ohio, 1997, and Lori Siebert and Lisa Ballard, Making a Good Layout, Northern Light Books, Cincinnati, Ohio, 1992, the contents of which are hereby incorporated by reference thereto, the preferred embodiment of the present invention extracted a number commonly mentioned principles used in evaluation of layout design, which include:
1) Balance—An equal distribution of weight on the page. This principle refers to the symmetry (or asymmetry) of the page with respect to color, size, shape, and texture.
2) Spacing—Describes the basic layout of images on a page. Spacing parameters can be used to describe the feel of a layout in terms of white space, or randomness.
3) Chronology—Describes whether the placement of images on the page matches with the temporal order in which the pictures were taken.
4) Emphasis—What stands out most gets noticed first. Emphasized elements will be the focal point of a page. Although generally achieved using element size, emphasis can also be maintained by use of color, shape, framing, and texture.
5) Unity—Elements that belong together look like they belong together. Unity is achieved by grouping, repetition, and the use of grids (a subdivision of space into rows, columns, and margins).
Each of the Page Creator Module 126 and Image Placement Module 132 judge the fitness of solutions based on a number of different criteria from the categories listed above. The user's preference for each criterion are input to each of the modules, as was noted respecting the AAS system diagram in
In addition to preference parameters, the Page Creator Module 126 and Image Placement Module 132 also utilize a set of importance parameters. These importance parameters define how important it is that the system, for a given criterion, obtains a solution where the layout produced evaluates to the exact value of the preference parameter for that criterion. Another way of looking at this is that the importance parameters indicate how much variation the system is allowed with respect to a given criterion. For example, one of the evaluation criteria for the Image Placement Module is white space. A preference of 1.0 indicates that a layout with a larger amount of white space is desired, whereas a preference of 0.0 is indicates that a layout with very little white space is preferred. Setting the preference for white space to be 0.0 with an importance of 1.0, the system will do everything it can to assure that the layout has as little white space as possible. Note that this is not the same as saying that the importance of white space is 0.0. In the above example, white space is very important, it's just that the user wants very little of it. An importance of 0.0 indicates to the system that it should not even pay attention to the white space preference and, as a result produce a solution with as much or little white space as the system deems appropriate.
Each module has a number of system parameters. These parameters are not directly used during the layout evaluation process. Instead, these parameters provide flexibility for the modules ensuring that they can be used by different albuming systems and in a variety of albuming situations. Examples of system parameters include page dimensions, resolution of the output device, minimum and maximum number of pages per album, etc.
The function of the Page Creator Module is to place each image onto an album page. Alternately, this module can be thought of as being responsible for assigning to each image a page number, where this number corresponds to the page on which the image will be placed. The Page Creator Module makes use of a number of system parameters. These parameters are listed in Table 1 below. As noted above, the values for these system parameters are set before the task of page creation commences. The symbol assigned to each parameter will be used in subsequent sections for referencing the values of these parameters.
The genome for the page creator module makes use of a tree structure as illustrated in
Standard crossover and mutation operators for tree structures are used by the Page Creator Module. These operators are illustrated in
The determination of fitness used by the Page Creator Module is a combination of a number of factors. First, a solution is evaluated and scored using a number of different criteria. For each criterion, the score achieved by the solution is compared to the preference of the user as defined by the preference parameters supplied to the module. This comparison results in a score indicating the suitability of the solution given the preferences of the user. Finally, the final fitness is obtained by scaling these suitability values on a criterion by criteria basis using the importance parameters, also supplied by the user.
In the following paragraphs, the fitness evaluation process is discussed in more detail. First a discussion of the evaluation criteria and judging procedure is given. Then the means by which the final fitness is obtained is discussed.
Evaluation Criteria—Solutions for the Page Creator Module are evaluated on the following four criteria:
For each criterion, a score ranging from 0.0 to 1.0 is given to each solution based on the evaluation. Details of the evaluation method for each criterion are given below.
Note that solutions containing a total number of pages outside the range determined by the system parameters PAGEmin-PAGEmax, are deemed as unfit solutions and given a final fitness of 0.0. Similarly, solutions that place a single image in multiple places, or solutions that do not consider all images are immediately assigned a fitness of 0.0.
Evaluation of Balance—The balance score is a measure of whether there are an equal number of images on each page. A solution with an equal number of images on all of its pages will obtain a perfect score of 1.0. The score is determined by, first, calculating the mean number of images per page. The average deviation from this mean amongst all the pages in the album is determined and scaled by a penalty factor (pbalance). Since a perfect score will have a deviation of 0.0, the final score is obtained by subtracting this scaled deviation value from 1.0.
Evaluation of Emphasis—In considering emphasis, the evaluation method attempts to judge how equally distributed the emphasis is amongst the pages in the album. It is undesirable to have too much emphasis on a single page since this will limit the capability of the image placement module to adequately emphasize the images that have high emphasis values. At the same time, a page with too little emphasis on a page will force the image placement module to emphasize images that don't properly deserve emphasis.
In the evaluation of emphasis, it is assumed that the sum of the emphasis values for all of the images on a “perfect” page will equal 1.0. This ideal value of 1.0 could be replaced by some value based on the average of the emphasis values amongst all of the images. An evaluation score for emphasis is obtained by calculating the percentage of pages whose emphasis sum amongst all of the images placed on it, are within an “acceptable” range. This “acceptable range” is defined to be 1.0±σemphasis.
Evaluation of Chronology—In evaluating chronology, each solution is judged based on how closely the order of the images as presented on the pages match that of the chronology of the images. To perform this evaluation, first define the notion of a “chronology range”. This range is defined as the range of chronology values for images that should appear on the page if the album was perfectly ordered in time. For example, given an album with three pages, with two images on the first page, four images on the second page, and three images on the final page, the chronology range for each page would be given according to Table 2:
When performing the evaluation for chronology, each page is given a score based on the percentage of images that fall within chronology range for the page. The final score is obtained by computing the average of the page scores over all of the pages in the album.
Evaluation of Unity—Evaluation of unity is performed by considering the images belonging to events and sub-events and their proximity to each other when constructing the pages for the album. Two types of scores are computed when evaluating unity:
1) Page Unity Score—Each page is evaluated to determine the percentage of images that belong to the same event that appear on the page. If all the images on a page belong to the same event or sub-event, the page unity score is 1.0. If several events share the same page, a penalty based on the fraction of events of which images appear on the page is applied. A total page unity score is obtained by adding the positive scores and subtracting penalty scores over all of the pages. This total page score is then calculated by dividing the sum by the number of pages in the album.
2) Event Unity—Each event is evaluated to determine the percentage of images in the same event that fall on the same or subsequent pages. For each page that images of a particular event appears, the fraction of images from that page that belong to that event (or sub-event) is determined. If an event totally dominates a page, a score of 1.0 is added to the total Event Unity score. Otherwise, the fraction of images on that page not belonging to the event in question is applied as a penalty and subtracted from the total Event Unity Score. It is acceptable for an event or sub-event to scan multiple pages. For those events that do, a bonus is applied when the pages on which the event images appear are subsequent. A penalty, based on the distance between the pages on which the images appear, is applied if this is not the case. Finally, the final event unity score is scaled by the best possible score given the number of events and the arrangement of the images amongst the pages.
The above scores can be calculated on an event and/or sub-event basis. The system allows one to define whether sub-event checking should be performed. The final unity score is a linear combination of scores for page and event unity for each evaluation performed. Individual scores are scaled equally in determination of the final unity score.
Calculation of Final Fitness—The evaluation algorithms listed above provide raw scores for each one of the evaluation criteria. In determining the final fitness of the solution, both the preferences and the importance of each criteria, as specified by the preference and importance parameters supplied by the user, must also be considered. To determine how well a solution meets the preferences of the user, the difference between the user preference and the raw score is calculated for each criterion. These differences are subtracted from 1.0 resulting in a fitness score for each criteria such that a fitness score of 1.0 indicates a good match with user preferences and a score of 0.0 indicated a bad match with user preferences.
The values of the importance parameters are used to scale the contribution of each criterion to the final fitness score. Given a set of importance parameter values, the best possible score obtainable for a given run of the algorithm can be calculated by assuming the fitness score for each criterion to be perfect (i.e. equal to 1.0). The final fitness score is determined by scaling each of the actual fitness scores for each criteria by the corresponding importance parameter value, summing the results from all of the criteria and dividing this sum by the best possible fitness value obtainable. This final fitness score can be summarized mathematically as:
To illustrate the relationship of these functions and clarify the process generally, what follows are several examples of using the Page Creator Module on a group of images. In each of the examples, the image set presented in
In each of tests illustrated in
The effects of balance are illustrated in
The effects of emphasis are illustrated in
Considering the solutions above, it is clear that unity and chronology tend to lump images together on the same page, whereas emphasis and balance tend to favor solutions where images are more spread out amongst pages. An interesting compromise is reached when combining the effects of two criteria. In
Now considering the Image Placement Module (item 134 in
In the following discussions, the positioning parameters (x, y, s, θ) are be used to indicate the calculated position of an image on the page with each member of the four factors corresponding to a respective value listed above. A set of four positioning parameters define, and are referred to as, the image position.
In performing the solution encoding and solution fitness evaluation, the Image Placement Module makes use of a number of system parameters. The values of these parameters will vary dependent on the application using the module and are set before layouts are processed by the module. A list of these system parameters is given in Table 3. The symbol assigned to each parameter will be used for reference hereinafter.
A floating point array is used as the genome for the image placement module. Reference is directed to
All elements of the array are floating point values ranging from 0.0 to 1.0. This is to assure that all genes (i.e. array elements) are considered equal when performing genetic operations. The value of the genes for a given image are be referred as genex, geney, genes, and geneθ for the value corresponding to the x position, y position, scaling, and rotation respectively. The mappings from these array element values to positioning parameter values for each positioning parameter are given hereinafter.
The x and y positioning parameters give the placement of the center of a given image on the album page. In the genome, this is expressed relative to the total height and width of the page with the origin being the upper-left corner of the page. Appropriate calculations are made by the Image Placement Module to assure that an image placement calculated from given gene values will not result in any part of the image being placed off the boundaries of the album page. The mapping from the gene values for x and y positioning to actual x and y position on the page can thus be given by:
where the width and height of the image in pixels are given by Wimage and Himage respectively.
Scaling is expressed in the gene by a linear ramp between the minimum allowable scaling and the maximum allowable scaling. The mapping from gene value to actual scale factor is thus given by:
The rotation positional parameter gives the rotation of an image about the center of the image with respect to the vertical axis of that image. Reference is directed to
The mapping from gene value to actual rotation angle can thus be given by:
In an illustrative embodiment, standard crossover and mutation operators for arrays are used by the Image Placement Module. These operators are illustrated in
Similar to the Page Creator Module, the fitness for the Image Placement Module is a combination of raw evaluations in a number of given criteria considered with respect to the preference and importance parameters supplied from the user preference database. The method for evaluation of fitness is outlined in the following discussion. The evaluation criteria for layout solutions include seven different criteria. These criteria can be categorized as follows.
For each criterion, a solution, represented by a genome, is given a score ranging from 0.0 to 1.0. In order to perform the evaluations, a mockup of the page layout is generated by decoding the genome values and positioning rectangles on a two-dimensional grid using a polygon-clipping library. In an illustrative embodiment of the present invention, the generic polygon clipping library according to the reference: Alan Murta, “A Generic Polygon Clipping Library”, Department of Computer Science, University of Manchester, 1999, the contents of which are hereby incorporated by reference thereto, is used. This mock layout is used to calculate the various area calculations used in the evaluations.
The evaluation of white space involves a score of the white space, which is a measure of the amount of white space on the page and is determined by calculating the percentage of the page area not filled by any images. A score of 0.0 indicates a layout where the images placed on the page take up the entire page area. A score of 1.0 is indicative of an empty page with no images on it (i.e. the entire page is white space). As indicated in Table 3, there are system parameters that limit the acceptable amount of white space allowed. Solutions that result in white space percentages below the minimum or greater than the maximum are tagged as unacceptable solutions and given a final fitness value of 0.0.
In evaluating overlap, the maximum overlap between any two images is considered. The overlap score is defined as the maximum percentage of any image area covered by another image over all of the images placed on the page. Similar to white space, there are system parameters that limit the acceptable amount of overlap allowed. See Table 3, above. Solutions that result in overlap scores below the minimum or greater than the maximum are tagged as unacceptable solutions and given a final fitness value of 0.0.
The rotation score is an indication of the total amount of image rotation on an album page. It is derived by averaging the absolute values of the image rotations over all of the images. This average is scaled by θmax to achieve a score between 0.0 and 1.0. Note that since the interpretation of the genome encoding for rotation ensures that the rotation for each image will be between −θmax and +θmax, this rotation evaluation score will never exceed 1.0.
Spatial balance is evaluated by comparing the image areas in the four quadrants of the album page. The page is first split horizontally into equal halves. The amount of page area containing images for both halves are determined and the ratio of the half with the smaller image area over the half with the larger image area is calculated. For a spatially balanced layout, this ratio will be close to 1.0. The same procedure is performed in the vertical direction. The final spatial balance score is the average of the two ratios.
The score for rotational balance is calculated in a manner similar to that of rotation. Like with the rotation evaluation, the rotation balance values over all the images are averaged. However, unlike the evaluation for rotation, the actual rotation values and not the absolute values of these rotations are considered when calculating the average. The rational behind this evaluation is that for a rotationally balanced layout, the summation of all the rotations should amount to 0.0. The rotational balance score is obtained by scaling the calculated signed average by θmax and then subtracting this value from 1.0. This way, a very rotationally balanced layout, (i.e. one where the signed average of the rotations is 0.0) will produce a score of 1.0. Similarly, a very rotationally unbalanced layout, one where the average of the rotations is close to θmax will earn a score of 0.0.
The border symmetry score evaluates how closely the edges of the image on the page form a natural boundary. For each edge of the album page (top, bottom, left, right), a border distance is determined by considering the image endpoint closest to the page edge and calculating the distance from the edge to the endpoint. The mean distance amongst the four edges is determined and the average distance from the mean amongst the four edges is calculated. This average is scaled by BDRmax and subtracted from 1.0 to generate the final score. Note that if the average distance is greater than BDRmax, the solution is flagged as unfit and is assigned a final fitness value of 0.0.
The emphasis score measures the proportionality of the size of the images with respect to the emphasis values assigned to the images. The rationale behind this evaluation stems from the notion that images with large emphasis values should take up more space on the page. The comparison made during evaluation is relative to the sizes of all of the images. For each image, the size relative to the largest image in the group is calculated and expressed as a percentage. This percentage is then subtracted from the emphasis value assigned to the image. Then mean difference amongst all the images on the page is calculated and this average is subtracted from 1.0, assuring that an emphasis score of 1.0 indicates a strong positive correlation between image size and emphasis values.
In a fashion similar to the approach used with the Page Creator Module, in determining the final fitness of the solution, both the preferences and the importance of each criterion, as specified by the preference and importance parameters supplied by the user, must also be considered. Thus, the Image Placement Module determines final fitness in the same manner as the page creator module described herein before. The only difference is in the set of criteria considered in performing the evaluation. Thus, final fitness for the Image Placement Module according to an illustrative embodiment of the present invention is described by:
where I represents the set of importance parameters value (1 per criteria), P represents the set of preference parameter values (1 per criteria) and E represents the set of raw evaluations scores as determined by the procedures outlined above (1 per criteria).
To gain a better understanding of the Image Placement Module layout functionality, it is beneficial to contemplate a series of exemplar page layouts, each of which exercises certain aspects of this module. These layouts appear in
The effect of rotation is illustrated in
The effects of spatial balance are shown in
Finally, the effects of emphasis are illustrated in
The variety of page layouts that can be produced by the Image Placement Module is best illustrated when modifying several preference parameters at once. In
Thus, the present invention has been described herein with reference to a particular embodiment for a particular application. Those having ordinary skill in the art and access to the present teachings will recognize additional modifications, applications and embodiments within the scope thereof. For example,
It is therefore intended by the appended claims to cover any and all such applications, modifications and embodiments within the scope of the present invention.