Measuring Similarity Between Matrix Objects for Pattern Recognition

Abstract

In order to make machines able to recognize various patterns, it is important to define an appropriate function for measuring similarities between different objects. Conventional similarity measures are devised mainly for 1D vector data, which may lead to loss of information of 2D matrix data. We cast the calculation of similarity between two matrices as a neural network problem, and design the architecture for learning a similarity measure. We provide experiments on real 2D matrix data in the face recognition and gesture recognition, where we show that the learning of a similarity measure leads to improvements in the performance of the recognition problem. Also we compare the performance of the proposed measure with conventional distance measures for 2D matrix data.