Content-Based Keyframe Clustering Using Near Duplicate Keyframe Identification

Ehsan Younessian and Deepu Rajan
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Ehsan Younessian: Nanyang Technological University, Singapore
Deepu Rajan: Nanyang Technological University, Singapore

International Journal of Multimedia Data Engineering and Management (IJMDEM), 2011, vol. 2, issue 1, 1-21

Abstract: In this paper, the authors propose an effective content-based clustering method for keyframes of news video stories using the Near Duplicate Keyframe (NDK) identification concept. Initially, the authors investigate the near-duplicate relationship, as a content-based visual similarity across keyframes, through the Near-Duplicate Keyframe (NDK) identification algorithm presented. The authors assign a near-duplicate score to each pair of keyframes within the story. 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.

Date: 2011
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International Journal of Multimedia Data Engineering and Management (IJMDEM) is currently edited by Chengcui Zhang

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