STATISTICAL MODELING FOR MOTION-BASED VIDEO CLASSIFICATION AND RETRIEVAL

We have developed an original approach for content-based video indexing and retrieval. By introducing a causal Gibbsian modeling of the spatio-temporal distribution of appropriate local motion-related measurements, we have designed a general and efficient statistical framework for non parametric motion modeling, motion recognition and classification, and motion segmentation. It is exploited for motionbased video indexing and video retrieval for both global and partial queries by example.

[1]  Anil K. Jain,et al.  Feature Selection: Evaluation, Application, and Small Sample Performance , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Patrick Bouthemy,et al.  Non-Parametric Motion Activity Analysis for Statistical Retrieval with Partial Query , 2004, Journal of Mathematical Imaging and Vision.

[3]  Patrick Bouthemy,et al.  Motion characterization from temporal cooccurrences of local motion-based measures for video indexing , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[4]  Randal C. Nelson,et al.  Qualitative recognition of motion using temporal texture , 1992, CVGIP Image Underst..

[5]  K. Pahlavan,et al.  Texture Modeling by Multiple Pairwise Pixel Interactions , 1996 .

[6]  Martin Szummer,et al.  Temporal texture modeling , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[7]  Patrick Pérez,et al.  Non Parametric Statistical Analysis of Scene Activity for Motion-Based Video Indexing and Retrieval , 2000 .

[8]  A. Murat Tekalp,et al.  Effective content representation for video , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[9]  Jean-Marc Odobez,et al.  Robust Multiresolution Estimation of Parametric Motion Models , 1995, J. Vis. Commun. Image Represent..

[10]  Ronan Fablet.  Modelisation statistique non parametrique et reconnaissance du mouvement dans des sequences d'images ; application a l'indexation video , 2001 .

[11]  Patrick Bouthemy,et al.  Non parametric motion recognition using temporal multiscale Gibbs models , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[12]  Thierry Pun,et al.  Correspondence analysis and hierarchical indexing for content-based image retrieval , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[13]  Patrick Bouthemy,et al.  Determining a Structured Spatio-Temporal Representation of Video Content for Efficient Visualization and Indexing , 1998, ECCV.

[14]  Ibrahim M. Elfadel,et al.  Gibbs Random Fields, Cooccurrences, and Texture Modeling , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Michal Irani,et al.  Video indexing based on mosaic representations , 1998, Proc. IEEE.

[16]  Simone Santini,et al.  Similarity Measures , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Dragutin Petkovic,et al.  Content-based representation and retrieval of visual media: A state-of-the-art review , 1996, Multimedia Tools and Applications.

[18]  Stephen W. Smoliar,et al.  An integrated system for content-based video retrieval and browsing , 1997, Pattern Recognit..

[19]  Patrick Bouthemy,et al.  Motion recognition using spatio-temporal random walks in sequence of 2D motion-related measurements , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).