Feature selection based on genetic algorithm for CBIR

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T. Zhao, J. Lu, Y. Zhang, Q. Xiao: Feature selection based on genetic algorithm for CBIR. IEEE Congress on Image and Signal Processing, 2, 2008, pp. 495–499.




Automated techniques to optimize feature descriptor weights and select optimum feature descriptor subset are desirable as a way to enhance the performance of content based image retrieval system. In our system, all the MPEG-7 image feature descriptors including color descriptors, texture descriptors and shape descriptors are used to represent low-level image features. We use a real coded chromosome genetic algorithm (GA) and k-nearest neighbor (k-NN) classification accuracy as fitness function to optimize weights. Meanwhile, a binary one and k-NN classification accuracy combining with the size of feature descriptor subset as fitness function are used to select optimum feature descriptor subset. Furthermore, we propose two kinds of two-stage feature selection schemes for weight optimization and descriptor subset selection, which are the integration of a real coded GA and a binary one. The experimental results over 2000 classified Corel images show that with weight optimization, the accuracy of image retrieval system is improved; with the selection of optimum feature descriptor subset, both the accuracy and the efficiency are improved.

Extended Abstract


author={T. Zhao and J. Lu and Y. Zhang and Q. Xiao},
booktitle={Image and Signal Processing, 2008. CISP '08. Congress on},
title={Feature Selection Based on Genetic Algorithm for CBIR},
keywords={Automation;Biological cells;Content based retrieval;Feature extraction;Genetic algorithms;Image retrieval;MPEG 7 Standard;Multimedia databases;Programmable logic arrays;Shape measurement;Feature selection;genetic algorithm;image retrieval;k-nearest neighbor classifier;multimedia content description interface},


url={http://dx.doi.org/10.1109/CISP.2008.90 http://de.evo-art.org/index.php?title=Feature_selection_based_on_genetic_algorithm_for_CBIR },

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