A new line symmetry distance based pattern classifier

Abstract

In this paper, a new line symmetry based classifier (LSC) is proposed to deal with pattern classification problems. In order to measure total amount of line symmetry of a particular point in a class, a new definition of line symmetry based distance is also proposed in this paper. The proposed line symmetry based classifier (LSC) utilizes this new definition of line symmetry distance for classifying an unknown test sample. LSC assigns an unknown test sample pattern to that class with respect to whose major axis it is most symmetric. The mean of all the training patterns belonging to that particular class is taken as the prototype of that class. Thus training constitutes of computing only the class prototypes and the major axes of those classes. Kd-tree based nearest neighbor search is used for reducing complexity of line symmetry distance computation. The performance of LSC is demonstrated in classifying twelve artificial and real-life data sets of varying complexities. Experimental results show that LSC achieves, in general, higher classification accuracy compared to k-NN classifier. Results indicate that the proposed novel line symmetry based classifier is well-suited for classifying data sets having symmetrical classes, irrespective of any convexity, overlap and size. Statistical analysis, ANOVA is also performed to compare the performance of these classifications techniques.