Content-based image classification with wavelet relevance vector machines

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.