Content-based image classification with wavelet relevance vector machines |
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Authors: | Arvind Tolambiya S. Venkataraman Prem K. Kalra |
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Affiliation: | (1) Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, UP, India |
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Abstract: | This paper introduces the use of relevance vector machines (RVMs) for content-based image classification and compares it with the conventional support vector machine (SVM) approach. Different wavelet kernels are included in the formulation of the RVM. We also propose a new wavelet-based feature extraction method that extracts lesser number of features as compared to other wavelet-based feature extraction methods. Experimental results confirm the superiority of RVM over SVM in terms of the trade-off between slightly reduced accuracy but substantially enhanced sparseness of the solution, and also the ease of free parameters tuning. |
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