Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ant Colony Optimization
Motion-Based Selection of Relevant Video Segments for Video Summarization
Multimedia Tools and Applications
Detection and representation of scenes in videos
IEEE Transactions on Multimedia
Automatic video summarizing tool using MPEG-7 descriptors for personal video recorder
IEEE Transactions on Consumer Electronics
An enhanced video summarization system using audio features for a personal video recorder
IEEE Transactions on Consumer Electronics
Rapid scene analysis on compressed video
IEEE Transactions on Circuits and Systems for Video Technology
MINMAX optimal video summarization
IEEE Transactions on Circuits and Systems for Video Technology
Rushes video summarization using a collaborative approach
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
ELVIS: Entertainment-led video summaries
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Content-Based Keyframe Clustering Using Near Duplicate Keyframe Identification
International Journal of Multimedia Data Engineering & Management
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Video summarization approaches have various fields of application, specifically related to organizing, browsing and accessing large video databases. In this paper we propose and evaluate two novel approaches for video summarization, one based on spectral methods and the other on ant-tree clustering. The overall summary creation process is broke down in two steps: detection of similar scenes and extraction of the most representative ones. While clustering approaches are used for scene segmentation, the post-processing logic merges video scenes into a subset of user relevant scenes. In the case of the spectral approach, representative scenes are extracted following the logic that important parts of the video are related with high motion activity of segments within scenes. In the alternative approach we estimate a subset of relevant video scene using ant-tree optimization approaches and in a supervised scenario certain scenes of no interest to the user are recognized and excluded from the summary. An experimental evaluation validating the feasibility and the robustness of these approaches is presented.