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|Title:||Video organizaation : near-duplicate video clustering||Authors:||Zhu, Ce
Tan, Yap Peng
|Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2012||Source:||Hung, T. Y., Zhu, C., Yang, G., & Tan, Y. P. (2012). Video organizaation : near-duplicate video clustering. 2012 IEEE International Symposium on Circuits and Systems, 1879-1882.||Abstract:||It is not uncommon to see several videos of almost identical content on the internet. These near duplicates, coupled with the sheer number of videos, pose a big challenge to the effective organization of video clips online. We propose an adaptive classification approach to detect near-duplicate versions, and an integrated voting strategy to group clusters and to elect a representative for each cluster. Our proposed methods are based on our observation that near-duplicate videos usually span a small, albeit variable area in the feature space, while videos of different contents are scattered far apart. The classification method aims to select a suitable threshold by maximizing the margin for each video sequence in the similarity space, and the voting scheme focuses on merging subsets with mutual information based on neighbor information and inverted indices. Experimental results on an unconstrained web dataset including over 10000 videos demonstrate the efficacy of the proposed methods.||URI:||https://hdl.handle.net/10356/103592
|DOI:||10.1109/ISCAS.2012.6271637||Rights:||© 2012 IEEE.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Conference Papers|
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