VisualGREP: a systematic method to compare and retrieve video sequences

In this paper, we consider the problem of similarity between video sequences. Three basic questions are raised and (partially) answered. Firstly, at what temporal duration can video sequences be compared? The frame, shot, scene and video levels are identified. Secondly, given some image or video feature, what are the requirements on its distance measure and how can it be 'easily' transformed into the visual similarity desired by the inquirer? Thirdly, how can video sequences be compared at different levels? A general approach based on either a set or sequence representation with variable degrees of aggregation is proposed and applied recursively over the different levels of temporal resolution. It allows the inquirer to fully control the importance of temporal ordering and duration. Promising experimental results are presented.

[1]  Yoshinobu Tonomura,et al.  Video tomography: an efficient method for camerawork extraction and motion analysis , 1994, MULTIMEDIA '94.

[2]  Boon-Lock Yeo,et al.  Extracting story units from long programs for video browsing and navigation , 1996, Proceedings of the Third IEEE International Conference on Multimedia Computing and Systems.

[3]  Ramin Zabih,et al.  A feature-based algorithm for detecting and classifying scene breaks , 1995, MULTIMEDIA '95.

[4]  Boon-Lock Yeo,et al.  Video content characterization and compaction for digital library applications , 1997, Electronic Imaging.

[5]  Ramin Zabih,et al.  Comparing images using color coherence vectors , 1997, MULTIMEDIA '96.

[6]  Ramesh C. Jain,et al.  Knowledge-guided parsing in video databases , 1993, Electronic Imaging.

[7]  Mohamed Abdel-Mottaleb,et al.  Content-based video retrieval by example video clip , 1997, Electronic Imaging.

[8]  David Bordwell,et al.  Film Art: An Introduction , 1979 .

[9]  Wolfgang Effelsberg,et al.  Automatic audio content analysis , 1997, MULTIMEDIA '96.

[10]  Graham A. Stephen String Searching Algorithms , 1994, Lecture Notes Series on Computing.

[11]  Harpreet S. Sawhney,et al.  Compact Representations of Videos Through Dominant and Multiple Motion Estimation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

[13]  Shih-Fu Chang,et al.  VisualSEEk: a fully automated content-based image query system , 1997, MULTIMEDIA '96.

[14]  Amarnath Gupta,et al.  Virage video engine , 1997, Electronic Imaging.

[15]  Simone Santini,et al.  Similarity Matching , 1995, ACCV.

[16]  Charles A. Poynton,et al.  A technical introduction to digital video , 1996 .

[17]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[18]  Stephen W. Smoliar,et al.  Developing power tools for video indexing and retrieval , 1994, Electronic Imaging.

[19]  D. D. Saur Automated analysis and annotation of basketball video. Storage and Retrieval for Image and Video Databases V , 1997 .

[20]  Jing Huang,et al.  Image indexing using color correlograms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[21]  Bernd Jähne,et al.  Digital Image Processing: Concepts, Algorithms, and Scientific Applications , 1991 .

[22]  Wolfgang Effelsberg,et al.  The MoCA Workbench: support for creativity in movie content analysis , 1996, Proceedings of the Third IEEE International Conference on Multimedia Computing and Systems.

[23]  Ramesh C. Jain,et al.  ImageGREP: fast visual pattern matching in image databases , 1997, Electronic Imaging.

[24]  Wolfgang Effelsberg,et al.  Video abstracting , 1997, CACM.