Image classification based on pyramid histogram of topics

Authors:
Fuxiang Lu;Xiaokang Yang;Rui Zhang;Songyu Yu
Affiliations:
Shanghai Key Lab. of Digital Media Processing and Transmission, Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai, China;Shanghai Key Lab. of Digital Media Processing and Transmission, Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai, China;Shanghai Key Lab. of Digital Media Processing and Transmission, Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai, China;Shanghai Key Lab. of Digital Media Processing and Transmission, Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai, China
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

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Abstract

In this paper we propose PHOTO (Pyramid Histogram Of TOpics), a new representation for image classification. We partition the image into hierarchical cells and learn the topic histogram using pLSA over each cell with EM algorithm. Then we concatenate the topic histograms over the cells at all levels to form a "long" vector, i.e. pyramid histogram of topics. Finally AdaBoost classifiers are used to select the topics most discriminative for class recognition. Experimental results on two diverse databases show that our method performs significantly better than general topic representation.