Image retrieval based on GA integrated color vector quantization and curvelet transform

Authors:
Yungang Zhang;Tianwei Xu;Wei Gao
Affiliations:
School of Information Science, Yunnan Normal University, Kunming, China,Department of Computer Science & Software Engineering, Xi'anJiaoTong-Liverpool University, Suzhou, China;School of Information Science, Yunnan Normal University, Kunming, China;Department of Mathematics, Soochow Univeristy, Suzhou, China
Venue:
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2012

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Abstract

Color and shape information have been two important image descriptors in Content Based Image Retrieval (CBIR) systems. The focus of this research is to find a method representing images with color and shape information in the way of human visual perception. The image retrieval approach proposed here depends on the color and shape features extracted by color Vector Quantization (VQ) and the Digital Curvelet Transform (DCT), respectively. The extracted color and shape features were combined and weighted by Genetic Algorithm (GA), then used for image similarity measurement. Experimental results show that the GA combined features can bring about improved image retrieval performance.