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 extr...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2010, Vol.14 (2), p.129-136
Main Authors: Tolambiya, Arvind, Venkatraman, S., Kalra, Prem K.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Approximation
Artificial Intelligence
Classification
Computational Intelligence
Control
Engineering
Feature extraction
Focus
Image classification
Image retrieval
Machine learning
Mathematical Logic and Foundations
Mechatronics
Robotics
Support vector machines
Wavelet transforms
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Summary: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.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-009-0439-8