In video information retrieval, key frame extraction has been rec ognized as one of the important research issues. Although much progress has been made, the existing approaches are either computationally expensive or ...In video information retrieval, key frame extraction has been rec ognized as one of the important research issues. Although much progress has been made, the existing approaches are either computationally expensive or ineffective in capturing salient visual content. In this paper, we first discuss the importance of key frame extraction and then briefly 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. Meanwhile, we provide a feedback chain to adjust the granularity of the extraction result. The proposed algorithm is both computationally simple and able to capture the visual content.The efficiency and effectiveness are validated by large amount of real-world videos.展开更多
文摘In video information retrieval, key frame extraction has been rec ognized as one of the important research issues. Although much progress has been made, the existing approaches are either computationally expensive or ineffective in capturing salient visual content. In this paper, we first discuss the importance of key frame extraction and then briefly 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. Meanwhile, we provide a feedback chain to adjust the granularity of the extraction result. The proposed algorithm is both computationally simple and able to capture the visual content.The efficiency and effectiveness are validated by large amount of real-world videos.
