Rx for semantic video database retrieval

ABSTRACT

The most prominent difference between still images and moving pictures stems from movements and variations. Thus to go from the realm of still image repositories to video databases, we must be able to deal with motion. Particularly, we need the ability to classify objects appearing in a video sequence based on the movements of each object, as well as their other characteristics and features such as shape or color.By describing motion derived from motion analysis, we introduce a dual hierarchy consisting of spatial and temporal parts for video sequence representation. This gives us the flexibility to examine arbitrary frames at various levels of abstraction, and to retrieve the associated temporal information (say, object trajectories) in addition to the spatial representation. Our algorithm for motion detection uses the motion compensation component of the MPEG video encoding scheme. The algorithm then computes trajectories for objects of interest. The specification of a language for retrieval of video based on the spatial as well as motion characteristics is presented.

REFERENCES

• 1: Mark Charles Allmen, Image sequence description using spatiotemporal flow curves: toward motion-based recognition, University of Wisconsin at Madison, Madison, WI, 1992
• 2: Nevenka Dimitrova. Motion Extraction and Video Database Retrieval. Technical Report MMIS-94-001, Computer Science Department, Arizona State University, 1994.
• 3: Richard O. Duds and Peter E. Hart. Pattern Classification and Scene Analysis. John Wiley and Sons, 1973.
• 4: Gerald Farin, Curves and surfaces for computer aided geometric design: a practical guide, Academic Press Professional, Inc., San Diego, CA, 1988
• 5: Nigel Hugh Goddard, The perception of articulated motion: recognizing moving light displays, University of Rochester, Rochester, NY, 1992
• 6: Forouzan Golshani and Nevenka Dimitrova. Retrieval and Delivery of Information in Multimedia Database Systems. Information and Software Technology, 36(4), May 1994.
• 7: W. I. Grosky , R. Mehrotra, Guest Editors' Introduction: Image Database Management, Computer, v.22 n.12, p.7-8, December 1989
• 8: Amarnath Gupta , Terry E. Weymouth , Ramesh Jain, Semantic Queries with Pictures: The VIMSYS Model, Proceedings of the 17th International Conference on Very Large Data Bases, p.69-79, September 03-06, 1991
• 9: Berthold K.P. Horn and Brian G. Schunck. Determining Optical Flow. Artificial Intelligence, 17:185-203, 1981.
• 10: Dieter Koller , Joseph Weber , Jitendra Malik, Robust Multiple Car Tracking with Occlusion Reasoning, University of California at Berkeley, Berkeley, CA, 1993
• 11: Hiroaki Kubota , Yasukazu Okamoto , Hiroshi Mizoguchi , Yoshinori Kuno, Vision processor system for moving-object analysis, Machine Vision and Applications, v.7 n.1, p.37-43, Winter 1993 [doi>10.1007/BF01212415]
• 12: Ketan Patel , Brian C. Smith , Lawrence A. Rowe, Performance of a software MPEG video decoder, Proceedings of the first ACM international conference on Multimedia, p.75-82, August 02-06, 1993, Anaheim, California, United States [doi>10.1145/166266.166274]
• 13: K. Rohr, Towards model-based recognition of human movements in image sequences, CVGIP: Image Understanding, v.59 n.1, p.94-115, Jan. 1994 [doi>10.1006/ciun.1994.1006]
• 14: Robert J. Schalkoff, Digital image processing and computer vision: an introduction to theory and implementations, John Wiley & Sons, Inc., New York, NY, 1989
• 15: I. K. Sethi , R. Jain, Finding trajectories of feature points in a monocular image sequence, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.9 n.1, p.56-73, Jan. 1987 [doi>10.1109/TPAMI.1987.4767872]
• 16: Deborah Swanberg, Chiao-Fe Shu, and Ramesh .lain. Knowledge Guided Parsing in Video Databases. In Proceedings SPIE IS f~ T Syrup. on Storage and Retrieval for Image and Video Databases, Bellingham, Washington, 1993.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.1 Multimedia Information Systems
          Subjects: Video (e.g., tape, disk, DVI)

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.10 Vision and Scene Understanding
            Subjects: Motion
  I.5 PATTERN RECOGNITION
      I.5.4 Applications
          Subjects: Computer vision

General Terms:
Algorithms, Languages