A novel scale and rotation invariant texture image retrieval method using fuzzy logic classifier |
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| Affiliation: | 1. Department of Electronics and Telecommunication Engineering, SVERI’s College of Engineering, Pandharpur, Maharashtra State, India;2. Department of Electronics Engineering, Walchand Institute of Technology, Solapur, Maharashtra State, India;3. Department of Electronics and Telecommunication Engineering, JSPM’s Rajarshi Shahu College of Engineering, Pune, Maharashtra State, India;1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430081, China;1. Department of Electrical and Electronics Engineering, Bilecik Seyh Edebali University, Bilecik 11210, Turkey;2. Department of Electrical and Electronics Engineering, Anadolu University, Eskisehir 26555, Turkey;1. College of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China;2. State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China;3. School of Physics and Engineering, Qufu Normal University, Qufu, Shandong 273165, China |
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| Abstract: | A novel approach for content-based texture image retrieval system using fuzzy logic classifier is proposed in this paper. The novelty of this method is demonstrated by handling the complexity issues in texture image retrieval arising from rotation and scale variance. These issues are divided into four groups as non rotated non scaled, rotation invariant, scale invariant and scale and rotation invariant texture retrieval for retrieval performance analysis. Features of texture images are obtained using discrete wavelet transform based statistical features and gray level co-occurrence matrix based co-occurrence features. The fuzzy logic classifier is developed with Gaussian membership function with mean and standard deviations of the features. The retrieval performance improvement is carried out by considering various combinations of the features. The average retrieval rates for the four issues have been achieved at 99.40% with 40 features, 91% with 80 features, 65.2% with 40 features, and 63.4% with 65 features respectively. This method outperforms the existing methods in terms of average retrieval rate. The scale and rotation invariant texture retrieval is an incomparable work that has been demonstrated in the present paper. |
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