Kobe University at TRECVID 2009 Search Task

In TRECVID 2009 search task, we have developed a method which defines any interesting topic from examples provided by a user, especially, positive and negative examples. Specifically, considering a large variation of featur es in a topic, we use “rough set theory” which defines the topic as a union of subsets. In each subset, some positive examples can be correctly distinguished from all negative examples. Based on such subsets, we can collectively retrieve shots which show the same topic but contain significantly different features.

[1]  Andrzej Skowron,et al.  Rough-Fuzzy Hybridization: A New Trend in Decision Making , 1999 .

[2]  Koen E. A. van de Sande,et al.  Evaluation of color descriptors for object and scene recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Shahram Ebadollahi,et al.  Visual Event Detection using Multi-Dimensional Concept Dynamics , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[4]  Chong-Wah Ngo,et al.  Video event detection using motion relativity and visual relatedness , 2008, ACM Multimedia.

[5]  Frédéric Jurie,et al.  Learning Visual Similarity Measures for Comparing Never Seen Objects , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  M. Ibrahim Sezan,et al.  A semantic event-detection approach and its application to detecting hunts in wildlife vide , 2000, IEEE Trans. Circuits Syst. Video Technol..

[7]  Jiawei Han,et al.  PEBL: Web page classification without negative examples , 2004, IEEE Transactions on Knowledge and Data Engineering.

[8]  Andrzej Skowron,et al.  Rough Sets: A Tutorial , 1998 .

[9]  Philip S. Yu,et al.  Building text classifiers using positive and unlabeled examples , 2003, Third IEEE International Conference on Data Mining.

[10]  Lei Zhang,et al.  A CBIR method based on color-spatial feature , 1999, Proceedings of IEEE. IEEE Region 10 Conference. TENCON 99. 'Multimedia Technology for Asia-Pacific Information Infrastructure' (Cat. No.99CH37030).

[11]  John R. Smith,et al.  Large-scale concept ontology for multimedia , 2006, IEEE MultiMedia.

[12]  Philip S. Yu,et al.  Text classification without negative examples revisit , 2006, IEEE Transactions on Knowledge and Data Engineering.

[13]  Philip S. Yu,et al.  Fast algorithms for projected clustering , 1999, SIGMOD '99.

[14]  Manfred M. Fischer,et al.  A Rough Set Approach for the Discovery of Classification Rules in Interval-Valued Information Systems , 2008, Int. J. Approx. Reason..

[15]  Marcel Worring,et al.  Multimedia event-based video indexing using time intervals , 2005, IEEE Transactions on Multimedia.

[16]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.