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Using an efficient keypoint matching technique followed by matching pattern analysis, this NDK identification algorithm can handle extreme zooming and significant object motion. In the second step, the weighted adjacency matrix is determined for each story based on assigned near duplicate score. The authors then use the spectral clustering scheme to remove outlier keyframes and partition remainders. Two sets of experiments are carried out to evaluate the NDK identification method and assess the proposed keyframe clustering method performance.<\/p>","DOI":"10.4018\/jmdem.2011010101","type":"journal-article","created":{"date-parts":[[2011,10,19]],"date-time":"2011-10-19T12:41:30Z","timestamp":1319028090000},"page":"1-21","source":"Crossref","is-referenced-by-count":0,"title":["Content-Based Keyframe Clustering Using Near Duplicate Keyframe Identification"],"prefix":"10.4018","volume":"2","author":[{"given":"Ehsan","family":"Younessian","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepu","family":"Rajan","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"jmdem.2011010101-0","doi-asserted-by":"crossref","unstructured":"Adami, N., Benini, S., & Leonardi, R. 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