Object classification with efficient global self-similarity descriptors based on sparse representations

Object recognition entails extracting information about which object class(es) are present in an image. In order to enhance the performance of object recognition, reducing the redundancy in the data is absolutely essential. Prior literature [1, 2] introduced local and global self-similarity features to highlight the areas in an image which are useful for object classification and detection. We introduce an efficient self-similarity measure based on sparse representations and propose two different descriptors. Our measure of self-similarity is determined across multiple scales and is more efficient than prior work. We test our self similarity descriptor using support vector machine based classification on the PASCAL VOC 2007 database consisting of 20 object classes. Comparative results indicate performance competitive with the prior approaches of computing self-similarity descriptors.