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.

[1]  Matthieu Cord,et al.  RETIN: A Content-Based Image Indexing and Retrieval System , 2001, Pattern Analysis & Applications.

[3]  Yixin Chen,et al.  Image Categorization by Learning and Reasoning with Regions , 2004, J. Mach. Learn. Res..

[4]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevan e Ve tor Ma hine , 2001 .

[5]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Robert M. Nishikawa,et al.  Relevance vector machine for automatic detection of clustered microcalcifications , 2005, IEEE Transactions on Medical Imaging.

[7]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[8]  Changchun Zhu,et al.  Wavelet Support Vector Machine With Universal Approximation and its Application , 2006, 2006 IEEE Information Theory Workshop - ITW '06 Chengdu.

[9]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2004 .

[10]  Li Zhang,et al.  Wavelet support vector machine , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  James A. Bucklew,et al.  Support vector machine techniques for nonlinear equalization , 2000, IEEE Trans. Signal Process..

[12]  Michael E. Tipping.  The Relevance Vector Machine , 1999, NIPS.

[13]  I. Nabney.  Efficient training of RBF networks for classification , 1999 .

[14]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[15]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Richard Johansson,et al.  Sparse Bayesian Classification of Predicate Arguments , 2005, CoNLL.

[17]  James Ze Wang,et al.  Content-based image indexing and searching using Daubechies' wavelets , 1998, International Journal on Digital Libraries.

[18]  José Luis Rojo-Álvarez,et al.  Kernel Methods in Bioengineering, Signal And Image Processing , 2007 .

[19]  Erkki Oja,et al.  PicSOM - content-based image retrieval with self-organizing maps , 2000, Pattern Recognit. Lett..

[20]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[21]  Jian-Huang Lai,et al.  Shannon Wavelet Kernel based Subspace LDA Approach in Face Recognition , 2006, 2006 International Conference on Computational Intelligence and Security.

[22]  Saeid Belkasim,et al.  Multi-Level Discrete Cosine Transform for Content-Based Image Retrieval by Support Vector Machines , 2007, 2007 IEEE International Conference on Image Processing.

[23]  Shih-Fu Chang,et al.  Image Retrieval: Current Techniques, Promising Directions, and Open Issues , 1999, J. Vis. Commun. Image Represent..

[24]  Andrew Blake,et al.  Sparse Bayesian learning for efficient visual tracking , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  David J. C. MacKay,et al.  The Evidence Framework Applied to Classification Networks , 1992, Neural Computation.