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Báo cáo hóa học: "Research Article Unsupervised Video Shot Detection Using Clustering Ensemble with a Color Global Scale-Invariant Feature Transform Descriptor"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Unsupervised Video Shot Detection Using Clustering Ensemble with a Color Global Scale-Invariant Feature Transform Descriptor | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2008 Article ID 860743 10 pages doi 10.1155 2008 860743 Research Article Unsupervised Video Shot Detection Using Clustering Ensemble with a Color Global Scale-Invariant Feature Transform Descriptor Yuchou Chang 1 D. J. Lee 1 Yi Hong 2 and James Archibald1 1 Electrical and Computer Engineering Department Brigham Young University Provo UT 84602 USA 2 Department of Computer Science City University of Hong Kong Kowloon HongKong Correspondence should be addressed to D. J. Lee djlee@ee.byu.edu Received 1 August 2007 Revised 30 October 2007 Accepted 22 November 2007 Recommended by Alain Tremeau Scale-invariant feature transform SIFT transforms a grayscale image into scale-invariant coordinates of local features that are invariant to image scale rotation and changing viewpoints. Because of its scale-invariant properties SIFT has been successfully used for object recognition and content-based image retrieval. The biggest drawback of SIFT is that it uses only grayscale information and misses important visual information regarding color. In this paper we present the development of a novel color feature extraction algorithm that addresses this problem and we also propose a new clustering strategy using clustering ensembles for video shot detection. Based on Fibonacci lattice-quantization we develop a novel color global scale-invariant feature transform CGSIFT for better description of color contents in video frames for video shot detection. CGSIFT first quantizes a color image representing it with a small number of color indices and then uses SIFT to extract features from the quantized color index image. We also develop a new space description method using small image regions to represent global color features as the second step of CGSIFT. Clustering ensembles focusing on knowledge reuse are then applied to obtain better clustering results than using single clustering methods for video shot .