Texture Image Retrieval Based on Contourlet Transform and Active Perceptual Similarity Learning

Authors:
Huaijing Qu;Yuhua Peng;Honglin Wan;Min Han
Affiliations:
School of Information Science and Engineering, Shandong University, Jinan, Shandong, People's Republic of China and School of Information & Electric Engineering, Shandong Jianzhu University, Jinan ...;School of Information Science and Engineering, Shandong University, Jinan, Shandong, People's Republic of China;School of Information Science and Engineering, Shandong University, Jinan, Shandong, People's Republic of China;School of Information Science and Engineering, Shandong University, Jinan, Shandong, People's Republic of China
Venue:
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Year:
2008

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Abstract

This paper proposes a new texture image retrieval scheme based on contourlet transform and support vector machines (SVMs). In the scheme, the energies and the generalized Gaussian distribution (GGD) parameters are used to represent the contourlet subband features. Using the representations, a two-run SVM retrieval algorithm which employs an one-class SVM followed by a two-class SVM is proposed to carry out the perceptual similarity measurement. For the query image, the one-class SVM is used to obtain the effective initial training set with positive and negative samples. Using these initial samples, the two-class SVM is applied to refine on the image classification subject to the user's relevance feedback. Compared with existing texture image retrieval methods, the proposed retrieval scheme is demonstrated respectively to be effective on the VisTex database of 640 texture images and the Brodatz database of 1760 texture images. Experimental results have shown that the proposed retrieval scheme can attain 99.38% and 98.07% of the average rates respectively for the two databases.