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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The authors would like to thank anonymous reviewers for their constructive comments.
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An erratum to this article can be found at http://dx.doi.org/10.1007/s00500-009-0477-2
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Tolambiya, A., Venkatraman, S. & Kalra, P.K. Content-based image classification with wavelet relevance vector machines. Soft Comput 14, 129–136 (2010). https://doi.org/10.1007/s00500-009-0439-8
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DOI: https://doi.org/10.1007/s00500-009-0439-8