Multiclass Test Feature Classifier for Texture Classification

Malaysian Journal of Computer Science

Vakthang Lashkia¹  Shun`ichi Kaneko¹  Satoru Igarashi¹  Itqon¹ 

关键词: Pattern recognition;    Test feature classifier;    Ill-class problem;    Texture classification;    Rank feature;   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: University of Malaya * Faculty of Computer Science and Information Technology

【 摘 要 】

A new multi-class pattern classifier called ‘Test Feature Classifier�? is presented.It is based on training a recogniser by training samples of binary patterns and voting primitive scores depending on many trained templates called ‘test feature�?, which serves as local evaluation of the features.The method is non-metric and does not misclassify any patterns once learned previously.The two-class version of test feature classifier was of high performance for searching textual region in complex images.In this paper, we extend it to handle multi-class problems and apply it for solving ill-class problems in texture classification.We show the performance of the classifier on more than 1000 real images and compare it with a linear distance-based classifier and a non-linear distance-based classifier.The experimental results of both simulations and real applications show that the proposed classifier has better performance than conventional ones.

【 授权许可】

Unknown