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Content-based image classification with wavelet relevance vector machines
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AI
DOI：10.1007/s00500-009-0439-8.png)
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Abstract
En 中文
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.
Keywords:
Relevance vector machine
Wavelet kernels
Image classification
Journal
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2.5
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Citations:
2.1W