Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Applications of Video-Content Analysis and Retrieval
IEEE MultiMedia
Adaptive clustering and interactive visualizations to support the selection of video clips
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
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We propose an innovative, general purpose, method to the selection and hierarchical representation of key frames of a video sequence for video summarization. It is able to create a hierarchical storyboard that the user may easily browse. The method is composed by three different steps. The first removes meaningless key frames, using supervised classification performed by a neural network on the basis of pictorial features and a visual attention model algorithm. The second step provides for the grouping of the key frames into clusters to allow multilevel summary using both low level and high level features. The third step identifies the default summary level that is shown to the users: starting from this set of key frames, the users can then browse the video content at different levels of detail