Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Kernels on bags for multi-object database retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Person spotting: video shot retrieval for face sets
CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval
Active Learning Methods for Interactive Image Retrieval
IEEE Transactions on Image Processing
Multimedia Tools and Applications
Fusing matching and biometric similarity measures for face diarization in video
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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This paper presents an actor video retrieval system based on face video-tubes extraction and representation with sets of temporally coherent features. Visual features, SIFT points, are tracked along a video shot, resulting in sets of feature point chains (spatio-temporal tubes). These tubes are then classified and retrieved using a kernel-based SVM learning framework for actor retrieval in a movie. In this paper, we present optimized feature tubes, we extend our feature representation with spatial location of SIFT points and we describe the new Spatio-Temporal Tube Kernel (STTK) of our content-based retrieval system. Our approach has been tested on a real movie and proved to be faster and more robust for actor retrieval task.