Abstract
This paper presents the effectiveness of applying genetic algorithm (GA)-based feature subset selection and parameter optimization of support vector machine (SVM) for content-based image retrieval (CBIR). SVM, one of the new techniques for pattern classification, has been widely used in many application areas. The kernel parameters setting for SVM in the training process impacts on the classification accuracy. Feature subset selection is another factor that impacts classification accuracy. The objective of this study is to simultaneously optimize the parameters and feature subset without degrading the SVM classification accuracy using the GA-based approach for CBIR. In this study, we show that the proposed GA-based approach outperforms SVM to the problem of the image classification problem in CBIR. Compared with NN and SVM algorithm, the proposed GA-based approach significantly improves the classification accuracy and has fewer input features for SVM.
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Seo, KK. (2007). A GA-Based Feature Subset Selection and Parameter Optimization of Support Vector Machine for Content – Based Image Retrieval. In: Alhajj, R., Gao, H., Li, J., Li, X., Zaïane, O.R. (eds) Advanced Data Mining and Applications. ADMA 2007. Lecture Notes in Computer Science(), vol 4632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73871-8_57
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DOI: https://doi.org/10.1007/978-3-540-73871-8_57
Publisher Name: Springer, Berlin, Heidelberg
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