New Automatic Clustering Method Based on the Dissemination of Binary Trees Applied to Video Segmentation


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Abstract


In order to manage the growing amount of video information efficiently, a video scene segmentation method is necessary. Many advanced video applications such as video on demand and digital library indexation also require the scene detection to organize the video content. In this paper we use clustering techniques in the video processing field to discover the video scene segmentation. Data clustering is a useful technique for the discovery of interesting data distributions and trends in the underlying data. The concentrated effort of the research community in the last few years resulted in many approaches for data clustering that progressed the field quickly in a few years. However, it remains more work to be done on non-parametric clustering techniques on large databases of high dimensionality. Through this feature, we have developed a novel method of non-parametric clustering based on the dissemination of binary trees structures on spatial representation of data. To assess the performance of this method, we compared with conventional clustering methods. We also showed its applications and advantages on video scene segmentation.
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Keywords


Clustering Algorithms; Trees of Dissemination; Density Based Clustering; Criteria of Validation; Dissimilarity Matrix; K-Means; Video Scene Segmentation

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