Computer Science ›› 2016, Vol. 43 ›› Issue (11): 19-23, 60.doi: 10.11896/j.issn.1002-137X.2016.11.004

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Representative Selection Framework Approach for Videos

JIANG Yong and ZHANG Hai-tao   

  • Online:2018-12-01 Published:2018-12-01

Abstract: In order to solve the process problem of massive videos data,a representative selection method of identifying the optimal subset of data points as a representative of original massive dataset was proposed.The selected data points of subset can represent inner structure of original massive dataset.And the novel representative selection method is based on 1-norm non-negative sparse graph for the original massive dataset.The massive data points are partitioned into some clusters by using a spectral clustering algorithm based on the non-negative sparse graph generated in previous steps.Each cluster is viewed as a point in the Grassmann manifold,and the geodesic distances among these points are measured.By using a min-max algorithm,geodesic distances are analyzed to build an optimal subset of clusters.Finally,the principal component centrality method is used to detect a representative after analyzing the sparse graph of selected clusters.The proposed framework is validated on the problem of video summarization,where a few key frames should be selected in long video clips which contain massive frames.The comparison of the results obtained between the proposed algorithm and some state-of-the-art methods was producted.Result indicates the effectiveness and feasibility of the proposed framework.

Key words: Sparse graph,Grassmann manifold,Geodesic distance,Key frame,Representative

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