A comparison of SVMs with MLC algorithms on texture features
Shuying Jin, Deren Li, Jianya Gong · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
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.