Adaptive Key Frame Extraction using Unsupervised Clustering

Key frame extraction has been recognized as one of the important research issues in video information retrieval. Although progress has been made in key frame extraction, the existing approaches are either computationally expensive or ine ective in capturing salient visual content. In this paper, we rst discuss the importance of key frame selection; and then brie y review and evaluate the existing approaches. To overcome the shortcomings of the existing approaches, we introduce a new algorithm for key frame extraction based on unsupervised clustering. The proposed algorithm is both computationally simple and able to adapt to the visual content. The e ciency and e ectiveness are validated by large amount of real-world videos.