Motion Activity Based Semantic Video Similarity Retrieval
PCM '02 Proceedings of the Third IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Dynamic selection and effective compression of key frames for video abstraction
Pattern Recognition Letters
Video similarity detection for digital rights management
ACSC '03 Proceedings of the 26th Australasian computer science conference - Volume 16
Automatic identification of digital video based on shot-level sequence matching
Proceedings of the 13th annual ACM international conference on Multimedia
Detection of video sequences using compact signatures
ACM Transactions on Information Systems (TOIS)
Shot-based video retrieval with optical flow tensor and HMMs
Pattern Recognition Letters
Robust video sequence retrieval using a novel object-based T2D-histogram descriptor
Journal of Visual Communication and Image Representation
Robust video retrieval using temporal MVMB moments
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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Shot-based classification and retrieval is very important for video database organization and access. We present a new approach: 'nearest feature line - NFL' used in shot retrieval. We look at key-frames in a shot as feature points to represent the shot in feature space. Lines connecting the feature points are further used to approximate the variations in the whole shot. The similarity between the query image and the shots in video database are measured by calculating the distance between the query image and the feature lines in feature space. To make it more suited to video data, we improved the original NFL method by adding constraints on the feature lines. Experimental results show that our improved NFL method is better than the traditional classification methods such as nearest neighbor (NN) and nearest center (NC).