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METHOD AND SYSTEM FOR ANALYZING
VIDEO CONTENT USING DETECTED TEXT
IN VIDEO FRAMES
CROSS-REFERENCE TO RELATED
The present invention is related to that disclosed in U.S. Provisional Patent Application No. 60/117,658, filed on Jan. 28, 1999, entitled "METHOD AND APPARATUS FOR DETECTION AND LOCALIZATION OF TEXT IN VIDEO," which is commonly assigned to the assignee of the present invention. The disclosure of this related provisional patent application is incorporated herein by reference for all purposes as if fully set forth herein.
TECHNICAL FIELD OF THE INVENTION
The present invention is directed, in general, to video processing systems and, more specifically, to a system for analyzing and characterizing a video stream based on the attributes of text detected in the content of the video.
BACKGROUND OF THE INVENTION
The advent of digital television (DTV), the increasing popularity of the Internet, and the introduction of consumer multimedia electronics, such as compact disc (CD) and digital video disc. (DVD) players, have made tremendous amounts of multimedia information available to consumers. As video content becomes readily available and products for accessing it reach the consumer market, searching, indexing and identifying large volumes of multimedia data becomes even more challenging and important.
Systems and methods for indexing and classifying video have been described in numerous publications, including: M. Abdel-Mottaleb et al., "CONIVAS: Content-based Image and Video Access System," Proceedings of ACM Multimedia, pp. 427-428, Boston (1996); S-F. Chang et al., "VideoQ: An Automated Content Based Video Search System Using Visual Cues," Proceedings of ACM Multimedia, pp. 313-324, Seattle (1994); M. Christel et al, "Informedia Digital Video Library," Comm. of the ACM, Vol. 38, No. 4, pp. 57-58 (1995); N. Dimitrova et al, "Video Content Management in Consumer Devices," IEEE Transactions on Knowledge and Data Engineering (Nov. 1998); U. Gargi et al., "Indexing Text Events in Digital Video Databases," International Conference on Pattern Recognition, Brisbane, pp. 916-918 (Aug. 1998); M. K. Mandal et al., "Image Indexing Using Moments and Wavelets," IEEE Transactions on Consumer Electronics, Vol. 42, No. 3 (Aug. 1996); and S. Pfeiffer et al., "Abstracting Digital Moves Automatically," Journal on Visual Communications and Image Representation, Vol. 7, No. 4, pp. 345-353 (1996).
The detection of advertising commercials in a video stream is an also active research area. See R. Lienhart et al., "On the Detection and Recognition of Television Commercials," Proceedings of IEEE International Conference on Multimedia Computing and Systems, pp. 509-516 (1997); and T. McGee et al., "Parsing TV Programs for Identification and Removal of Non-Story Segments," SPIE Conference on Storage and Retrieval in Image and Video Databases, San Jose (Jan. 1999).
Recognition of text in document images is well known in the art. Document scanners and associated optical character recognition (OCR) software are widely available and well understood. However, detection and recognition of text in video frames presents unique problems and requires a very
different approach than does text in printed documents. Text in printed documents is usually restricted to single-color characters on a uniform background (plain paper) and generally requires only a simple thresholding algorithm to
5 separate the text from the background. By contrast, characters in scaled-down video images suffer from a variety of noise components, including uncontrolled illumination conditions. Also, the background frequently moves and text characters may be of different color, sizes and fonts.
1° The extraction of characters by local thresholding and the detection of image regions containing characters by evaluating gray-level differences between adjacent regions has been described in "Recognizing Characters in Scene Images," Ohya et al., IEEE Transactions on Pattern Analysis
15 and Machine Intelligence, Vol. 16, pp. 214-224 (Feb. 1994). Ohya et al. further discloses the merging of detected regions having close proximity and similar gray levels in order to generate character pattern candidates.
Using the spatial context and high contrast characteristics
20 of video text to merge regions with horizontal and vertical edges in close proximity to one another in order to detect text has been described in "Text, Speech, and Vision for Video Segmentation: The Informedia Project," by A. Hauptmann et al., AAAI Fall 1995 Symposium on Computational
25 Models for Integrating Language and Vision (1995). R. Lienhart and F. Suber discuss a non-linear red, green, and blue (RGB) color system for reducing the number of colors in a video image in "Automatic Text Recognition for Video Indexing," SPIE Conference on Image and Video Processing
30 (Jan. 1996). A subsequent split-and-merge process produces homogeneous segments having similar color. Lienhart and Suber use various heuristic methods to detect characters in homogenous regions, including foreground, characters, monochrome or rigid characters, size-restricted characters,
and characters having high contrast in comparison to surrounding regions.
Using multi-valued image decomposition for locating text and separating images into multiple real foreground and
4Q background images is described in "Automatic Text Location in Images and Video Frames," by A. K. Jain and B. Yu, Proceedings of IEEE Pattern Recognition, pp. 2055-2076, Vol. 31 (Nov. 12, 1998). J-C. Shim et al. describe using a generalized region-labeling algorithm to find homogeneous
45 regions and to segment and extract text in "Automatic Text Extraction from Video for Content-Based Annotation and Retrieval," Proceedings of the International Conference on Pattern Recognition, pp. 618-620 (1998). Identified foreground images are clustered in order to determine the color
5Q and location of text.
Other useful algorithms for character segmentation are described by K. V. Mardia et al. in "A Spatial Thresholding Method for Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 10, pp.
55 919-927 (1988), and by A. Perez et al. in "An Iterative Thresholding Method for Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 9, pp. 742-751 (1987).
The prior art text-recognition systems do not take into
60 account, however, the non-semantic attributes of text detected in the content of the video. The prior art systems simply identify the semantic content of the image text and index the video clips based on the semantic content. Other attributes of the image text, such as physical location in the
65 frame, duration, movement, and/or temporal location in a program are ignored. Additionally, no attempt has been made to use video content to identify and edit video clips.
There is therefore a need in the art for improved video processing systems that enable a user to search through an archive of video clips and to selectively save and/or edit all or portions of video clips that contain image text attributes that match image text attributes selected by a user. 5
SUMMARY OF THE INVENTION
To address the above-discussed deficiencies of the prior art, the present invention discloses a video processing device for searching or filtering video streams for one or more 10 user-selected image text attributes. Generally, "searching" video streams refers to searching in response to user-defined inputs, whereas "filtering" generally refers to an automated process that requires little or no user input. However, in the disclosure, "searching" and "filtering" may be used inter- 15 changeably. An image processor detects and extracts image text from frames in video clips, determines the relevant attributes of the extracted image text, and compares the extracted image text attributes and the user-selected image text attributes. If a match occurs, the video processing 20 device may modify, transfer, label or otherwise identify at least a portion of the video stream in accordance with user commands. The video processing device uses the userselected image text attributes to search through an archive of video clips to 1) locate particular types of events, such as 25 news programs or sports events; 2) locate programs featuring particular persons or groups; 3) locate programs by name; 4) save or remove all or some commercials, and to otherwise sort, edit, and save all of, or portions of, video clips according to image text that appears in the frames of 30 the video clips.
It is a primary object of the present invention to provide, for use in a system capable of analyzing image text in video frames, a video processing device capable of searching and/or filtering video streams in response to receipt of at least one selected image text attribute. In an exemplary embodiment, the video processing device comprises an image processor capable of receiving a first video stream comprising a plurality of video frames, detecting and extracting image text from the plurality of video frames, determining at least one attribute of the extracted image text, comparing the at least one extracted image text attribute and the at least one selected image text attribute, and, in response to a match between the at least one extracted image text attribute and the at least one selected image text attribute, at least one of: 1) modifying at least a portion of the first video stream in accordance with a first user command; 2) transferring at least a portion of the first video stream in accordance with a second user command; and 3) labeling at least a portion of the first video stream in accordance with a third user command.
According to an exemplary embodiment of the present invention, the at least one extracted image text attribute indicates that the image text in the plurality of video frames 5J is one of: scrolling horizontally; scrolling vertically; fading, special effects and animation effects.
According to one embodiment of the present invention, the at least one extracted image text attribute indicates that the image text in the plurality of video frames is one of: a g0 name of a person; and a name of a group.
According to another embodiment of the present invention, the at least one extracted image text attribute indicates that the image text in the plurality of video frames is part of a commercial advertisement. 65
According to still another embodiment of the present invention, the at least one extracted image text attribute
indicates that the image text in the plurality of video frames is text appearing at one of: a start of a program; and an end of a program.
According to yet another embodiment of the present invention, the at least one extracted image text attribute indicates that the image text in the plurality of video frames is part of a program name.
According to a further embodiment of the present invention, the at least one extracted image text attribute indicates that the image text in the plurality of video frames is part of a news program.
According to a still further embodiment of the present invention, the at least one extracted image text attribute indicates that the image text in the plurality of video frames is part of a sports program.
The foregoing has outlined rather broadly the features and technical advantages of the present invention so that those skilled in the art may better understand the detailed description of the invention that follows. Additional features and advantages of the invention will be described hereinafter that form the subject of the claims of the invention. Those skilled in the art should appreciate that they may readily use the conception and the specific embodiment disclosed as a basis for modifying or designing other structures for carrying out the same purposes of the present invention. Those skilled in the art should also realize that such equivalent constructions do not depart from the spirit and scope of the invention in its broadest form.
Before undertaking the DETAILED DESCRIPTION, it may be advantageous to set forth definitions of certain words and phrases used throughout this patent document: the terms "include" and "comprise," as well as derivatives thereof, mean inclusion without limitation; the term "or," is inclusive, meaning and/or; the phrases "associated with" and "associated therewith," as well as derivatives thereof, may mean to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, or the like; and the term "processor" or "controller" means any device, system or part thereof that controls at least one operation, such a device may be implemented in hardware, firmware or software, or some combination of at least two of the same. It should be noted that the functionality associated with any particular controller may be centralized or distributed, whether locally or remotely. Additionally, the term "video clip" may mean a video segment, a video sequence, video content, or the like. Definitions for certain words and phrases are provided throughout this patent document. Those of ordinary skill in the art should understand that in many, if not most instances, such definitions apply to prior, as well as future uses of such defined words and phrases.
BRIEF DESCRIPTION OF THE DRAWINGS
For a more complete understanding of the present invention, and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, wherein like numbers designate like objects, and in which:
FIG. 1 illustrates an exemplary image text analysis system in accordance with one embodiment of the present invention;
FIG. 2 is a flow diagram illustrating a text extraction and recognition operation of exemplary video processing device in FIG. 1 according to one embodiment of the invention;