Feature classification for representative photo selection

Authors:
Wei-Ta Chu;Chia-Hung Lin;Jen-Yu Yu
Affiliations:
National Chung Cheng University, Chiayi, Taiwan Roc;National Chung Cheng University, Chiayi, Taiwan Roc;Industrial Technology Research Inst., Hsinchu, Taiwan Roc
Venue:
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Year:
2009

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Abstract

This paper points out that different local feature points provide different impacts to near-duplicate detection and related applications. Aiming to automatic representative photo selection, we develop three feature classification methods, i.e., point-based, region-based, and pLSA-based classification, to differentiate local feature points described by SIFT descriptors. We investigate the performance of these classification methods, and discuss how they influence near-duplicate detection and extended applications. Experiments show that, with effective feature classification, more accurate representative selection results can be achieved.