A comparison of SVMs with MLC algorithms on texture features

Shuying Jin; Deren Li; Jianya Gong

doi:10.1117/12.655313

3 November 2005 A comparison of SVMs with MLC algorithms on texture features

Shuying Jin, Deren Li, Jianya Gong

Proceedings Volume 6044, MIPPR 2005: Image Analysis Techniques; 60442B (2005) https://doi.org/10.1117/12.655313
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China

Abstract

A study is presented concerning the performance of support vector machines (SVMs) and maximum likelihood classification (MLC) algorithms on texture features. A novel multivariate modeling method--partial least square regression (PLSR) is applied to obtain novel texture features from texture spectrum (TS). Three texture features, together with PLSR-combined TS features, are used in Brodatz texture classification tests. The experiments show: 1) SVM has higher classification precisions and better generalization abilities than MLC no matter what texture features used and more suits to small training set size (TSS) situations; 2) the new proposed feature combination method (PLSR) can greatly improve TS features discrimination ability for MLC, but not for SVM.

Citation Download Citation

Shuying Jin, Deren Li, and Jianya Gong "A comparison of SVMs with MLC algorithms on texture features", Proc. SPIE 6044, MIPPR 2005: Image Analysis Techniques, 60442B (3 November 2005); https://doi.org/10.1117/12.655313

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