Content-based image retrieval by combining genetic algorithm and support vector machine

Content-based image retrieval (CBIR) is an important and widely studied topic since it can have significant impact on multimedia information retrieval. Recently, support vector machine (SVM) has been applied to the problem of CBIR. The SVM-based method has been compared with other methods such as neural network (NN) and logistic regression, and has shown good results. Genetic algorithm (GA) has been increasingly applied in conjunction with other At techniques. However, few studies have dealt with the combining GA and SVM, though there is a great potential for useful applications in this area. This paper focuses on simultaneously optimizing the parameters and feature subset selection for SVM without degrading the SVM classification accuracy by combining GA for CBIR. In this study, we show that the proposed approach outperforms the image classification problem for CBIR. Compared with NN and pure SVM, the proposed approach significantly improves the classification accuracy and has fewer input features for SVM.