WO2000014619A2 - Multi-linearization data structure for image browsing - Google Patents
Multi-linearization data structure for image browsing Download PDFInfo
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- WO2000014619A2 WO2000014619A2 PCT/US1999/020278 US9920278W WO0014619A2 WO 2000014619 A2 WO2000014619 A2 WO 2000014619A2 US 9920278 W US9920278 W US 9920278W WO 0014619 A2 WO0014619 A2 WO 0014619A2
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- images
- curve
- displaying
- linearization
- nearest neighbors
- Prior art date
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- 238000000034 method Methods 0.000 claims abstract description 43
- 238000013507 mapping Methods 0.000 claims description 13
- 230000006870 function Effects 0.000 description 9
- 238000005192 partition Methods 0.000 description 8
- 239000011800 void material Substances 0.000 description 5
- 230000009977 dual effect Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 238000003491 array Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T1/00—General purpose image data processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99941—Database schema or data structure
- Y10S707/99944—Object-oriented database structure
- Y10S707/99945—Object-oriented database structure processing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99941—Database schema or data structure
- Y10S707/99948—Application of database or data structure, e.g. distributed, multimedia, or image
Definitions
- the described invention relates to the field of data management.
- the invention relates to image categorization and display.
- Browsing is a technique that users employ to view information rapidly for deciding whether the content is relevant to their needs. Browsing should give the user quick access to view the content and focus on sections of further interest. Multiple images are typically displayed. The user is able to scroll through the images until the desired image is found.
- a method of displaying images is based on both a first linearization and a second linearization.
- the linearizations are performed by traversing two space-filling curves.
- the linearizations are performed by traversing a cluster data structure. More than two linearizations may be displayed.
- Figure 1 shows one embodiment of a flowchart of the process of creating dual linearizations for each image.
- Figure 2 shows an example of a space- filling curve.
- Figure 3 shows an embodiment in which a second curve is employed to improve the reliability of providing images that are close together in the multidimensional space.
- Figure 4 shows an example of a browser interface based on a dual linearization.
- Figure 5 shows an example of a browser interface based on a triple linearization.
- Figure 6 shows an example of a system implemented on an SQL server that communicates with a middleware server to construct and serve web pages to an end user.
- Figure 7 shows the 1st, 2nd, 3rd and 4th orders of a two-dimensional Hubert curve.
- Figure 8 shows the 1st, 2nd, and 3rd orders of a two-dimensional Peano curve.
- Linearization in this application, implies not just an ordering of a set of points, but also storing their positions, or at least relative distances between their positions.
- the method described herein is scaleable, so that the amount of time to categorize and display a portion of a set of images does not change significantly whether the set of images is relatively small (e.g., a few hundred images) or relatively large (e.g., greater than 50,000 images).
- An image may be categorized by various attributes, or components, of the image. For example, an image can be broken down and characterized by color information, shape information, size information, texture information, objects represented, or based on other content of the image.
- the image may also be categorized by tag information, which is information associated with each image, but which may not be part of the image itself.
- Tag information may include annotation information, audio information, time stamp information, global position information (GPS), and so forth.
- GPS global position information
- a histogram of an image is generated using 600 different color bins. The histogram includes the number of pixels corresponding to each of the 600 colors used.
- each of the components of the histogram is used to map the image to a coordinate in N-dimensional space.
- N is 600.
- Image 1 for example, is mapped to the coordinate:
- attribute 2 is the number of pixels corresponding to a second color
- Images that are located close together in the N-dimensional space are closely related by their attributes. In contrast, those that are located farther apart in the N-dimensional space are less closely related by their attributes.
- One method that has been used for ordering a set of images mapped into N- dimensional space is by mapping all of the images to an arbitrary line.
- the position on the line to which the images are mapped indicates the linearization of the images.
- This linearization does not preserve the relationship of similarly attributed images. This is because the mapping of the N-dimensional space into a one dimension line does not maintain information about how closely images are related in the N- dimensional space.
- One embodiment of the present invention linearizes the images using a spacefilling curve to help maintain the relationship of like images.
- a space-filling curve is a continuous path that visits every point in a k-dimensional space.
- the space-filling curve provides a way to linearize the points in the multi-dimensional space.
- Space-filling curves have been studied for many years, and more recently have interested computer scientists. For example, see “Fractals for Secondary Key Retrieval" by C. Faloutsos and S. Roseman (Technical Report CS-TR-2242, University of Maryland Institute for Advanced Computer Studies, May 1989) for an example of the use of space- filling curves.
- space filling curves There are several different types of space filling curves including the Hubert curve, the Peano curve, the Sierpinski curve, the Lebesgue curve, and the Schoenberg curve. See Hans Sagan, “Space-Filling Curves,” Springer- Verlag, 1994, for more information on space-filling curves.
- Figure 7 shows the 1st, 2nd, 3rd and 4th orders of a two-dimensional Hubert curve.
- the procedure to derive higher orders of the Hubert curve is to rotate and reflect the curve at vertex 0 and at vertex 3.
- the curve can keep growing recursively by following the same rotation and reflection pattern at each vertex of the curve.
- Figure 8 shows the 1st, 2nd, and 3rd orders of a two-dimensional Peano curve.
- the procedure to derive higher orders of the Peano curve is to replace each vertex of the basic curve with the previous order curve.
- the Hubert curve and Peano curves can be extended into multiple dimensions, as is well-known in the art.
- Space filling curves have the property that the points that are close to each other on the space-filling curve are also close to each other in the multi-dimensional space. However, the opposite is not true: the points that are close to each other in the multi-dimensional space are not necessarily close together on the space-filling curve.
- Figure 2 shows an example of a two-dimensional and three-dimensional spacefilling curve.
- an N-dimensional Hubert curve is employed.
- Two points 10 and 11 that are close to each other in the multi-dimensional space lie close to a partition boundary separating two sections of the multi-dimensional space.
- the curve is traversed as shown at the bottom of Figure 2, the points that were close in space map to points that lie far apart in linearization. This undesirable distancing of close points occurs at, and because of, partition boundaries of the Hubert and Peano curves.
- Figure 3 shows an embodiment in which a second curve is employed to improve the reliability of providing images that are close together in the multidimensional space.
- a Peano curve is used to complement a Hubert curve.
- the Peano and Hubert curves are constructed such that their partition boundaries, where undesirable results are likely to happen, occur in different locations. Points 20 and 21 that lie close in space will likely end up close together in either the Hubert curve or the Peano curve, as shown in Figure 3.
- Table 1 shows an example of a computer program written in C language that maps a point in N-dimensional space to a position on a one-dimensional traversal of a Hubert curve and a position on a one-dimensional traversal of a Peano curve.
- the code maps the point in N-dimensional space to an interval between 0 and 1.
- the output of the program is a bit pattern identifying a number between 0 and 1 for each image being analyzed. As other images are converted into other bit patterns between 0 and 1 , a database is set up with a first linearization based upon the Hubert curve and a second linearization based upon the Peano curve.
- images that map to a partition boundary are adjusted slightly so that they do not fall directly on the partition boundary. This is done because a curve may hit points on a partition boundary multiple times.
- the partition boundaries are at points that are any multiple of _ , _, 1/8, 1/16, etc, of the N-dimensional space.
- the partition boundaries are at points that are any multiple of 1/3, 1/9, 1/27, etc., of the N-dimensional space.
- the "points" are arrays of plain old integer coordinates. These are combined to produce a single value, indicating a position along a line.
- Sort images along Hilbert curve positions output sorted list
- Sort images along Peano curve positions output sorted list
- tau->end (tau->end + roller)%DIMENSION
- TOGGLE first bit of tau: replace 1 with 0, 0 with 1;
- COMPUTE k the XOR of the first j bits of tau
- SET trit 2-trit; /* Sets 2 to 0, or 0 to 2 */ OUTPUT trit;
- Source code for collapsing points in a space to points in a line, via space-filling curve traversal.
- the "points" are arrays of plain old integer coordinates. These are combined to produce a single value, indicating a position along a line.
- the hopper is a small buffer of digits in some small base b, such
- FIG. 1 shows one embodiment of a flowchart of the process of creating dual linearizations for each image.
- a histogram H(Ij) is created of image Ij.
- One implementation of the histogram is by using color information.
- Other feature information like shape and size can also be implemented, and can be in the form of histogram or other feature representation.
- any image content that can be represented as a vector may be used. Points "close” to one another are similar in that particular kind of content.
- the histogram of k attributes is mapped as a point p(j) in k- dimensional space, where k is any positive integer.
- the position of p(j) is computed in a Hilbert traversal of the k-space. In one embodiment, the position of p(j) is computed along an interval [0,1] in a Hilbert traversal of the k-space.
- the position of p(j) is computed along interval [0,1] in a Peano traversal of the k-space.
- p(j)'s four nearest neighbors based on the values obtained from the Hilbert and Peano curve traversals are located.
- a linked data structure is used to store each image as it is linearized. For example, a new image is placed into a first linked structure by its linearization based on the Hilbert curve traversal. The new image is linked to its nearest neighbor, i.e., the images closest to the new image in linearization. The new image is similarly placed into a second linked structure by its linearization based on the Peano curve traversal.
- a second advantage of using the described data structure is that it does not need to be completely recalculated each time an image is added or deleted from the data structure. Instead, only a few links need to be modified for each addition or deletion of an image.
- a second advantage of the data structure is that it is data independent, so that two existing databases can be combined into one with little more than a simple list merge.
- Clustering is another method of providing a linearization to images.
- a cluster is a grouping of images using a tree-like structure. The nature of clusters is such that it is not unusual that like images are placed into the data structure at the same time. By performing a linearization based upon a smart traversal of the cluster, like images are more likely to be placed next to each other in the linearization.
- One way of linearizing a cluster is to add an entire first group of images before traversing the cluster to add another group of images. For example, if a first group of images was all added to the cluster at the same time, these images should be linked together prior to moving on to a group of images that was added to the cluster at a different time than the first group.
- the attributes of the target image are compared with attributes of all groups of the cluster.
- the groups that are the closest in attributes are linearized first. Other groups are added based on their similarity in attributes.
- a centroid, or representative item having the average characteristics of an entire group, can be used to compare the target image with an entire group within the cluster.
- Another way to perform a linearization is to traverse the cluster in an orderly fashion. For example, a first linearization can traverse the cluster taking all right branches prior to taking any left branches. A second linearization can traverse the cluster taking all left branches. Alternatively, a first linearization can alterate between right and left branches, and a second linearization can alternate in the opposite manner.
- multiple linearizations can be created by first performing a first traversal of a cluster by traversing a first random traversal of the cluster linearizing each of the images of a section before moving on to a different section. A second traversal of the entire cluster is then performed by a different random traversal of the cluster.
- Figure 4 shows an example of a browser interface based on a dual linearization.
- a total of 53 images are displayed at a time.
- the current image of focus, or the target image is centered in the display.
- the images immediately to the left and right are the nearest neighbors 42a in one linearization.
- the images immediately to the top and bottom are the nearest neighbors 42b in a second linearization.
- the nearest neighbors are the images closest to the target image in linearization.
- the nearest neighbors correspond to the points that are closest in position in a linearization to the point corresponding to the target image.
- the first linearization may be based on a Hilbert curve
- the second linearization may be based on a Peano curve.
- the first linearization is based on a first traversal of a cluster data structure
- the second lienarization is based on a second traversal of the cluster data structure.
- the rules of displaying nearest neighbors of an image are applied recursively to all other images. Additionally, in one embodiment, images further away from the center, or target image, are displayed with a smaller size to denote greater dissimilarity from those images closer to the center.
- the next nearest neighbors 43 to the target image are smaller than the nearest neighbors 42a and 42b.
- the next-next nearest neighbors 44 of the target image are even smaller then its next nearest neighbors 43.
- Figure 5 shows an example of a browser interface based on a triple linearization.
- the images above and below an image are the nearest neighbors based on a first linearization.
- Two other images may correspond to nearest neighbors based on a second linearization, and the remaining two images may correspond to nearest neighbors based on a third linearization.
- more linearizations may be added, and a mixture of linearizations based on different methods is possible.
- Figure 6 shows an example of a system implemented on an SQL server that communicates with a middleware server to construct and serve web pages to an end user.
- the SQL server maintains the data structure of the multiple linearizations.
- the web server allows easy selection of the target image from any of the displayed images. When one of the images is selected, it becomes the target image, and the nearest neighbor and next-nearest neighbor images are updated correspondingly.
- the method steps of Figure 1 may be performed by a computer processor executing instructions organized into a program module or a custom designed state machine.
- Storage devices suitable for tangibly embodying computer program instructions include all forms of non-volatile memory including, but not limited to: semiconductor memory devices such as EPROM, EEPROM, and flash devices; magnetic disks (fixed, floppy, and removable); other magnetic media such as tape; and optical media such as CD-ROM disks.
- semiconductor memory devices such as EPROM, EEPROM, and flash devices
- magnetic disks fixed, floppy, and removable
- other magnetic media such as tape
- optical media such as CD-ROM disks.
- the methods described herein may be embodied in a hardware device such as a printed circuit board comprising discrete logic, integrated circuits, or specially designed application specific integrated circuits (ASIC).
- ASIC application specific integrated circuits
Abstract
Description
Claims
Priority Applications (4)
Application Number | Priority Date | Filing Date | Title |
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DE19983528A DE19983528B3 (en) | 1998-09-09 | 1999-09-03 | Multi-linearization data structure for image browsing |
JP2000569299A JP4298172B2 (en) | 1998-09-09 | 1999-09-03 | Multiple linearized data structures for image browsing |
GB0105288A GB2356962B (en) | 1998-09-09 | 1999-09-03 | Multi-linearization data structure for image browsing |
AU58076/99A AU5807699A (en) | 1998-09-09 | 1999-09-03 | Multi-linearization data structure for image browsing |
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US09/150,496 US6233367B1 (en) | 1998-09-09 | 1998-09-09 | Multi-linearization data structure for image browsing |
US09/150,496 | 1998-09-09 |
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WO2000014619A2 true WO2000014619A2 (en) | 2000-03-16 |
WO2000014619A3 WO2000014619A3 (en) | 2000-06-08 |
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JP (1) | JP4298172B2 (en) |
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AU (1) | AU5807699A (en) |
DE (1) | DE19983528B3 (en) |
GB (1) | GB2356962B (en) |
TW (1) | TW497083B (en) |
WO (1) | WO2000014619A2 (en) |
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US7016553B2 (en) | 1998-09-09 | 2006-03-21 | Intel Corporation | Linearized data structure ordering images based on their attributes |
WO2008013392A1 (en) * | 2006-07-25 | 2008-01-31 | Thinkware Systems Corporation | Hilbert space filling curve generating method and geographic information providing apparatus and system using that |
CN113342917A (en) * | 2021-06-29 | 2021-09-03 | 维沃移动通信有限公司 | Spatial position searching method and device, electronic equipment and storage medium |
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CN113342917A (en) * | 2021-06-29 | 2021-09-03 | 维沃移动通信有限公司 | Spatial position searching method and device, electronic equipment and storage medium |
Also Published As
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US7016553B2 (en) | 2006-03-21 |
KR20010086384A (en) | 2001-09-10 |
WO2000014619A3 (en) | 2000-06-08 |
DE19983528B3 (en) | 2005-02-03 |
US6628846B2 (en) | 2003-09-30 |
US20030174905A1 (en) | 2003-09-18 |
GB2356962B (en) | 2003-09-03 |
JP4298172B2 (en) | 2009-07-15 |
GB2356962A (en) | 2001-06-06 |
GB0105288D0 (en) | 2001-04-18 |
JP2002524789A (en) | 2002-08-06 |
TW497083B (en) | 2002-08-01 |
AU5807699A (en) | 2000-03-27 |
US20010004407A1 (en) | 2001-06-21 |
US20010014181A1 (en) | 2001-08-16 |
US6556723B2 (en) | 2003-04-29 |
US6233367B1 (en) | 2001-05-15 |
KR100380200B1 (en) | 2003-04-11 |
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