Non-sequential multiscale content-based video decomposition
Signal Processing - Special section on content-based image and video retrieval
Animation movies trailer computation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
PIDALION: a reconfigurable agent-based multimedia search engine platform
MAMECTIS'08 Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
Scalable keyframe extraction using one-class support vector machine
ICCS'03 Proceedings of the 2003 international conference on Computational science
Human action annotation, modeling and analysis based on implicit user interaction
Multimedia Tools and Applications
Implicit visual concept modeling in image / video annotation
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
EURASIP Journal on Advances in Signal Processing
Video abstraction based on the visual attention model and online clustering
Image Communication
Robust human action recognition scheme based on high-level feature fusion
Multimedia Tools and Applications
Multimedia Tools and Applications
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An efficient and low complexity algorithm for non-sequential video content representation is proposed. Our method is based on extracting a set of limited but meaningful frames (key-frames), able to represent the video content. The temporal variation of feature vectors for all frames within a shot, which form a trajectory in a high dimensional space, is used for key-frame selection. In particular, key-frames are extracted by estimating appropriate curve points, able to characterize the feature trajectory. The magnitude of the second derivative of the frame feature vectors with respect to time is used as a curvature measure in our approach. Due to low complexity of the algorithm, the proposed method can be easily implemented in hardware devices of even low processing capabilities thus can be embedded in many consumer electronics systems. For feature vector formulation, the video is first analyzed and several descriptors are extracted using a multiscale implementation of the recursive shortest spanning tree (RSST) algorithm, which significantly reduces the segmentation complexity. In addition, the whole procedure exploits information that exists in MPEG video databases so as to achieve a faster implementation. Finally, the extracted descriptors are classified using a fuzzy formulation scheme. Experimental results to real-life video sequences are presented to indicate the good performance of the proposed algorithm