視訊資訊系統結構化視訊計算與查詢方法

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Structured Video Computing and Query Methods in a Video Information System

 : NSC 87-2213-E009-093

 :  8681  87731

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(keyword: Video Indexing and Searching, Video Server, Video Information System, Video Feature Extraction)

In the near future, video information will be represented digitally and stored together on a device such as a hard disk or CD-ROM, and accessed directly by the computer. Processing and computing of dynamic digital video will be important for video data. One of the significant task is the automatic detection of video structure and the construction of structured video. Structured video will help the understanding of the scenario information in the video, recognition of objects in the scene and their relationships with other objects.

In this proposal, we present several important methods in a video information system for video object extraction and query processing. We first describe an efficient method for computing the position of one or more moving objects in a video sequence. We utilize computer vision routines for both target object tracking and feature value extraction. Then, we propose a multiple subsequence matching mechanism for comparing the difference of object motion between two video sequences. The definitions of mapping function, penalty table, and overall distance between two video sequences are provided. We use the prominent feature points as our segmentation positions and partition the sequence into several subsequence groups. After an alignment process, we can find the mapping of the best matching, and get an evaluation report between the two video sequences for temporal content analysis.

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Value A Light Blue (sky) 160~192 26~49 63~8

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[1] S. Adah, K. S. Candan, S. H. Chen, K. Erol and V. S. Subrahmanian, “The Advanced Video Information System: Data Structures and Query Processing,” Multimedia Systems, Vol. 4, No. 3, pp. 172-186, 1996.

[2] C. W. Chang, K. F. Lin and S. Y. Lee, "The Characteristics of Digital Video and Considerations of Designing Video Databases," Fourth Intl. Conf. on Information and Knowledge Management CIKM'95, pp. 370-377, Baltimore, Maryland, USA, 1995.

[3] C. W. Chang and S. Y. Lee, “Indexing and Approximate Matching for Content-based Time-series Data in Video Database,” First Intl. Conference on Visual Information Systems Visual’96, pp. 567-576, Melbourne, Australia, 1996.

[4] C. Faloutsos, M. Ranganathan and Y. Manolopoulos, “Fast Subsequence Matching in Time-Series Databases,” ACM SIGMOD, pp. 419-429, Minneapolis, MN., USA, 1994.

[5] Flickner et al., “Query by Image and Video Content: The QBIC System,” IEEE Computer, pp. 23-32, Sep. 1995.

[6] D. E. Knuth, J. H. Morris and V. R. Pratt, "Fast Pattern Matching in Strings," SIAM J. Comput., Vol. 6, pp. 323-350, Jun. 1977.

[7] A. D. Narasimhalu, “Special Section on Content-based Retrieval,” Multimedia Systems, Vol. 3, No. 1, pp. 1-2, Feb. 1995.

[8] A. D. Narasimhalu, “Multimedia Databases,” Multimedia Systems, Vol. 4, No. 5, pp. 226-249, 1996.

[9] S. W. Smoliar and H. Zhang, "Content-Based Video Indexing and Retrieval," IEEE Multimedia, pp. 62-72, Summer 1994.

[10] S. Stevens and T. Little, “Introduction to the Special Issue on Video Information Retrieval,” ACM Trans. on Information System, Vol. 13, No. 4, pp. 371-372, 1995.

[11] Y. Tonomura, A. Akutsu, Y. Taniguchi and G. Suzuki, "Structured Video Computing," IEEE Multimedia, Vol. 1, pp. 34-43, Fall 1994.

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