Study of image retrieval and classification based on adaptive features using genetic algorithm feature selection |
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| Affiliation: | 1. Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, No. 129, Sec. 3, Sanmin Rd., Taichung, Taiwan, ROC;2. Department of Electrical Engineering, National Chung Hsing University, No. 250 Kuo Kuang Rd., Taichung, Taiwan, ROC;1. Department of Electronics Convergence Engineering, Wonkwang University, 344-2, Shinyong-Dong, Iksan, Jeonbuk 570-749, South Korea;2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta T6G 2G7, Canada;3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;1. Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2950, Valparaíso, Chile;2. Universidad Finis Terrae, Av. Pedro de Valdivia 1509, Santiago, Chile;3. Universidad de Playa Ancha, Av. Leopoldo Carvallo 270, Valparaíso, Chile;4. Universidad Autónoma de Chile, Pedro de Valdivia 641, Santiago, Chile;5. CNRS, LINA, University of Nantes, 2 rue de la Houssinière, Nantes, France;6. Escuela de Ingeniería Industrial, Universidad Diego Portales, Manuel Rodríguez Sur 415, Santiago, Chile;1. Department of Management and Marketing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region;2. Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region;1. Department of Systems Engineering and Engineering Management, City University of Hong Kong, 83 Tat Chee Avenu, Kowloon Tong, Hong Kong;2. Centre for Systems Informatics Engineering, City University of Hong Kong, 83 Tat Chee Avenu, Kowloon Tong, Hong Kong;3. School of Management, Hefei University of Technology, Hefei, Box 270, Hefei 230009, Anhui, PR China;4. Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, Box 270, Hefei 230009, Anhui, PR China |
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| Abstract: | This paper proposes a genetic algorithm feature selection (GAFS) for image retrieval systems and image classification. Two texture features of adaptive motifs co-occurrence matrix (AMCOM) and gradient histogram for adaptive motifs (GHAM) and color feature of an adaptive color histogram for K-means (ACH) were used in this paper. In this paper, the feature selections have adopted sequential forward selection (SFS), sequential backward selection (SBS), and genetic algorithms feature selection (GAFS). Image retrieval and classification performance mainly build from three features: ACH, AMCOM and GHAM, where the classification system is used for two-class SVM classification. In the experimental results, we can find that all the methods regarding feature extraction mentioned in this study can contribute to better results with regard to image retrieval and image classification. The GAFS can provide a more robust solution at the expense of increased computational effort. By applying GAFS to image retrieval systems, not only could the number of features be effectively reduced, but higher image retrieval accuracy is elicited. |
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| Keywords: | Color feature Texture features Genetic algorithms Feature selection Support vector machine |
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