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Principles of Visual Information Retrieval pp 163–196Cite as

Video Indexing and Understanding

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Part of the book series: Advances in Pattern Recognition ((ACVPR))

Abstract

More and more video is generated every day. While today much of this data is produced and stored in analog form, the tendency is to use the digital form. The digital form allows processing of the video data in order to generate appropriate data abstractions that enable content-based retrieval of video. In the future, video databases will be able to be searched with combined text and visual queries. Additionally, video clips will be retrieved from longer sequences in large databases on the basis of the semantic video content. Ideally, the video will also be automatically annotated as a result of the machine interpretation of the semantic content of the video.

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References

  1. “Britannica Online” http://www.eb.com: 180.

    Google Scholar 

  2. Adjeroh, D, Lee, M, and Orji, C, “Techniques for Fast Partitioning of Compressed and Uncompressed Video”, Multimed Tools Applic, 4(3), pp. 225–243, 1997.

    Article  Google Scholar 

  3. Ahanger, A and Little, T, “A Survey of Technologies for Parsing and Indexing Digital Video”, J Visual Commun Image Represent, 7(1), pp. 28–43, 1996.

    Article  Google Scholar 

  4. Aigrain, P and Joly, P, “The Automatic Real-Time Analysis of Film Editing and Transition Effects and Its Applications”, Computers Graphics, 18(1), pp. 93–103, 1994.

    Article  Google Scholar 

  5. Arman, F, Hsu, A, and Chiu, M, “Image Processing on Encoded Video Sequences”, Multimed Syst, pp. 211-219, 1994.

    Google Scholar 

  6. Bolle, R, Yeo, B, and Yeung, M, “Video Query: Research Directions”, Multimed Syst, 42(2), pp. 233–252, 1998.

    Google Scholar 

  7. Chen, J, Taskiran, C, Delp, E, and Bouman, C, “ViBE: A New Paradigm for Video Database Browsing and Search”, IEEE Workshop on Content-Based Access of Image and Video Database, 1998.

    Google Scholar 

  8. Corridoni, JM and Del Bimbo, A, “Structured Digital Video Indexing”, Int. Conf. Pattern Recognition, 1996.

    Google Scholar 

  9. Davenport, G, Smith, TA, and Pincever, N, “Cinematic Primitives for Multimedia”, IEEE Computer Graphics Applic, 6(5), pp. 67–74, 1991.

    Article  Google Scholar 

  10. DeMethon, D, Kobla, V, and Doermann, D, “Video Summarization by Curve Simplification”, ACM Multimed, 1998.

    Google Scholar 

  11. Ding, W, Marchionini, G, and Tse, T, “Previewing Video Data: Browsing Key Frames at High Rates”, Int. Symp. on Digital Libraries, 1997.

    Google Scholar 

  12. Flicker, M, Sawhney, H, Niblack, W, Ashley, J, Huang, Q, Dom, B, Gorkani, M, Hafner, J, Lee, D, Petkovic, D, Steele, D, and Yanker, P, “Query by Image and Video Content: The QBIC System”, IEEE Computer, 28(9), pp. 23–32, 1995.

    Article  Google Scholar 

  13. Gauch, S, Gauch, J, and Pua, K, “VISION: A Digital Video Library”, ACM Digital Libraries, 1996.

    Google Scholar 

  14. Hampapur, A, Digital Video Indexing in Video Databases, PhD Thesis, University of Michigan, 1994.

    Google Scholar 

  15. Hampapur, A, Jain, R, and Weymouth, T, “Digital Video Segmentation”, ACM Multimedia, pp. 357-364, 1994.

    Google Scholar 

  16. Idris, F and Panchanathan, S, “Indexing of Compressed Video Sequences”, Proc. SPIE 2670: Storage and Retrieval for Image and Video Databases IV, pp. 247-253, 1996.

    Google Scholar 

  17. Idris, F and Panchanathan, S, “Review of Image and Video Indexing Techniques”, J Visual Commun Image Represent, 8(2), pp. 146–166, 1997.

    Article  Google Scholar 

  18. Jain, RC, “Segmentation of Frame Sequence Obtained by a Moving Observer”, IEEE Trans Patt Anal Mach Intell, 6(5), pp. 624–629, 1984.

    Article  Google Scholar 

  19. Jain, R and Hampapur, A, “Metadata in Video Databases”, ACM SIGMOD, 23 (4), 1994.

    Google Scholar 

  20. Kuo, T, Lin, Y, Chen, A, Chen, S, and Ni, C, “Efficient Shot Change Detection on Compressed Video Data”, Proc. Int. Workshop on Multimedia Database Management Systems, 1996.

    Google Scholar 

  21. Lee, J and Dickinson, BW, “Multiresolution Video for Subband Coded Video Databases”, Proc. SPIE: Storage and Retrieval for Image and Video Databases, 1994.

    Google Scholar 

  22. Lee, SY and Kao, HM, “Video Indexing: An Approach Based on Moving Object and Track”, Proc. SPIE 1908: Storage and Retrieval for Image and Video Databases, pp. 25-36, 1993.

    Google Scholar 

  23. Little, T, Ahanger, G, Folz, R, Gibbon, J, Reeves, F, Schelleng, D, and Venkatesh, D, “A Digital On-Demand Video Service Supporting Content-based Queries”, ACM Multimedia, pp. 427-433, 1993.

    Google Scholar 

  24. Liu, HC and Zick, GL “Scene Decomposition of MPEG Compressed Video”, Proc. SPIE 2419: Digital Video Compression, pp. 26-37, 1995.

    Google Scholar 

  25. Mandai, M, Idris, F, and Panchanathan, S, “A Critical Evaluation of Image and Video Indexing Techniques in the Compressed Domain”, Image Vision Comput, 17, pp. 513–529, 1999.

    Article  Google Scholar 

  26. Meng, J, Juan, Y, and Chang, SF, “Scene Change Detection in a MPEG Compressed Video Sequence”, Proc. SPIE 2419: Digital Video Compression, pp. 267-272, 1995.

    Google Scholar 

  27. Mohan, R, “Text Based Indexing of TV News Stories”, Proc. SPIE 2916: Multimedia Storage and Archiving Systems, 1996.

    Google Scholar 

  28. Nagasaka, A and Tanaka, Y, “Automatic Video Indexing and Full-Video Search for Object Appearances”, Visual Database Systems II, pp. 113–127, 1992.

    Google Scholar 

  29. Otsuji, K, Tonomura, Y, and 0hba, Y, “Video Browsing using Brightness Data”, Proc SPIE 1606: Visual Communications and Image Processing, pp. 980-989, 1991.

    Google Scholar 

  30. Patel, N and Sethi, I, “Video Shot Detection and Characterization for Video Databases”, Patt Recogn, 30(4), pp. 583–592, 1997.

    Article  Google Scholar 

  31. Poncelon, D, Srinivasan, S, Amir, A, and Petkovic, D, “Key to Effective Video Retrieval: Effective Cataloging and Browsing”, ACM Multimed, 1998.

    Google Scholar 

  32. Rowe, LA, Boreczky, JS, and Eads, CA, “Indices for User Access to Large Video Databases”, Proc. SPIE 2185: Storage and Retrieval for Image and Video Databases II, 1994.

    Google Scholar 

  33. Sawhney, H and Ayer, S, “Compact Representations of Videos Through Dominant and Multiple Motion Estimation”, IEEE Trans Patt Anal Mach Intell, 18(8), pp. 814–830, 1996.

    Article  Google Scholar 

  34. Sawhney, N, Balcom, D, and Smith, I, “Authoring and Navigating Video in Space and Time”, IEEE Multimed J, 1997.

    Google Scholar 

  35. Shahraray, B and Gibbon, D, “Automatic Generation of Pictorial Transcripts of Video Programs”, Proc. SPIE 2417: Multimedia Computing and Networking, 1995.

    Google Scholar 

  36. Shahraray, B, “Scene Change Detection and Content-Based Sampling of Video Sequence”, Proc. SPIE 2419: Digital Video Compression, pp. 2-13, 1995.

    Google Scholar 

  37. Shen, B, Li, D, and Sethi, IK, “Cut Detection via Compressed Domain Edge Extraction”, IEEE Workshop on Nonlinear Signal and Image Processing, 1997.

    Google Scholar 

  38. Szeliski, R, “Image Mosaicking for Tele-Reality Applications”, ACM Multimedia, 1993.

    Google Scholar 

  39. Taniguchi, Y, Akutsu, A, and Tonomura, Y, “PanoramaExcerpts: Extracting and Packing Panoramas for Video Browsing”, ACM Multimedia, 1997.

    Google Scholar 

  40. Taskiran, C and Delp, E, “Video Scene Change Detection Using the Generalized Trace”, IEEE Int. Conf. on Acoustic, Speech and Signal Processing, pp. 2961-2964, 1998.

    Google Scholar 

  41. Teodosio, L and Bender, W, “Salient Video Stills: Content and Context Preserved”, TR. DEC Cambridge Research Laboratory, 1994.

    Google Scholar 

  42. Tonomura, Y, Akutsu, A, Otsuji, K, and Sadakata, T, “VideospaceIcon: Tools for Anatomizing Video Content”, ACM INTERCHI, 1993.

    Google Scholar 

  43. Yeo, BL and Liu, B, “Rapid Scene Analysis on Compressed Video”, IEEE Trans Circuits Syst Video Technol, 5(6), pp. 533–544, 1995.

    Article  Google Scholar 

  44. Yeung, MM and Liu, B, “Efficient Matching and Clustering of Video Shots”, International Conf. Image Processing, pp. 338-341, 1995.

    Google Scholar 

  45. Yeung, MM, Yeo, B, Wolf, W, and Liu, B, “Video Browsing Using Clustering and Scene Transitions on Compressed Sequences”, Proc. SPIE 2417: Multimedia Computing and Networking, pp. 399-413, 1995.

    Google Scholar 

  46. Yeung, MM and Yeo, B, “Video Visualization for Compact Presentation of Pictorial Content”, IEEE Trans Circuits Syst Video Technol, 7(5), pp. 771–785, 1997.

    Article  Google Scholar 

  47. Yeung, MM and Yeo, B, “Video Content Characterization and Compaction for Digital Libraries”, Proc. SPIE 3022: Storage and Retrieval for Image and Video Databases V, pp. 45-58, 1997.

    Google Scholar 

  48. Zabih, R, Miller, J, and Mai, K, “A Feature-Based Algorithm for Detecting and Classifying Scene Breaks”, ACM Multimed, 1995.

    Google Scholar 

  49. Zhang, HJ, Kankanhalli, A, and Smoliar, SW, “Automatic Partitioning of Full-Motion Video”, Multimed Syst, 3(1), pp. 10–28, 1993

    Article  Google Scholar 

  50. Zhang, HJ, Tan, Y, Smoliar, SW, and Gong, Y, “Automatic Parsing and Indexing of News Video”, Multimed Syst, 6(2), pp. 256–266, 1995.

    Article  Google Scholar 

  51. Zhang, HJ, Low, CY, and Smoliar, SW, “Video Parsing and Browsing using Compressed Data”, Multimed Tools Applic, 1, pp. 89–111, 1995.

    Article  Google Scholar 

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Authors
  1. Michael S. Lew
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  2. Nicu Sebe
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  3. Paul C. Gardner
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Editor information

Editors and Affiliations

  1. Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands

    Michael S. Lew PhD

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© 2001 Springer-Verlag London

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Lew, M.S., Sebe, N., Gardner, P.C. (2001). Video Indexing and Understanding. In: Lew, M.S. (eds) Principles of Visual Information Retrieval. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-3702-3_7

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  • DOI: https://doi.org/10.1007/978-1-4471-3702-3_7

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84996-868-3

  • Online ISBN: 978-1-4471-3702-3

  • eBook Packages: Springer Book Archive

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