An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Algorithms for Interactive Multimedia
IEEE MultiMedia
On clustering and retrieval of video shots through temporal slices analysis
IEEE Transactions on Multimedia
Joint semantics and feature based image retrieval using relevance feedback
IEEE Transactions on Multimedia
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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Structuralization of the video stream is necessary for effective handling of video media. Especially, collecting similar scenes with sufficient accuracy contributes to the structuralization. In this paper, we propose a method of clustering with relevance feedback. First, fixed-length segments are clustered according to the feature of each segment. For the results of clustering, the user gives the feedback information whether each element is relevant to the cluster it belongs to. The accuracy can be improved by re-clustering based on the feedback information. Applying this method to a variety of video streams, we demonstrated the effectiveness of the relevance feedback.