Video abstraction based on the visual attention model and online clustering

With the fast evolution of digital video, research and development of new technologies are greatly needed to lower the cost of video archiving, cataloging and indexing, as well as improve the efficiency and accessibility of stored video sequences. A number of methods to respectively meet these requirements have been researched and proposed. As one of the most important research topics, video abstraction helps to enable us to quickly browse a large video database and to achieve efficient content access and representation. In this paper, a video abstraction algorithm based on the visual attention model and online clustering is proposed. First, shot boundaries are detected and key frames in each shot are extracted so that consecutive key frames in a shot have the same distance. Second, the spatial saliency map indicating the saliency value of each region of the image is generated from each key frame and regions of interest (ROI) is extracted according to the saliency map. Third, key frames, as well as their corresponding saliency map, are passed to a specific filter, and several thresholds are used so that the key frames containing less information are discarded. Finally, key frames are clustered using an online clustering method based on the features in ROIs. Experimental results demonstrate the performance and effectiveness of the proposed video abstraction algorithm.

► Video abstraction based on the visual attention model and online clustering.
► Representing important content in video by ROIs with high-level semantic information.
► Discarding unimportant content to eliminate the influence of insignificant changes.
► Using online clustering to reduce semantic redundancies and time costs.