A comparison of support vector machine with maximum likelihood classification algorithms on texture features

被引：0
|
作者：
Jin, SY [1 ]
Li, DR [1 ]
Wang, JW [1 ]
机构：
[1] Wuhan Univ, Wuhan 430072, Peoples R China
来源：
IGARSS 2005: IEEE International Geoscience and Remote Sensing Symposium, Vols 1-8, Proceedings | 2005年
关键词：
SVMs; texture classification; feature extraction; partial least square regression;
D O I：
暂无
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
A study is presented concerning the performance of support vector machines (SVMs) and maximum likelihood (ML) classification algorithms on texture features. A novel multivariate modeling method-partial least square regression (PLSR) is applied to get orthogonal components from texture spectrum features. Three texture features, together with the above components, are used in Brodatz texture classification tests. The experiments show: 1) SVMs perform better than ML classifier. 2) PLSR can improve the texture spectrum-based features discrimination ability with ML classifier. 3) Not one of the texture features performs best on all test images.
引用
收藏
页码：3717 / 3720
页数：4
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