Collaborative sparse representation in dissimilarity space for classification of visual information

Abstract

In this work we perform a thorough evaluation of the most popular CR-based classification scheme, the SRC, on the task of classification in dissimilarity space. We examine the performance utilizing a large set of public domain dissimilarity datasets mainly derived from classification problems relevant to visual information. We show that CR-based methods can exhibit remarkable performance in challenging situations characterized by extreme non-metric and non-Euclidean behavior, as well as limited number of available training samples per class. Furthermore, we investigate the structural qualities of a dataset necessitating the use of such classifiers. We demonstrate that CR-based methods have a clear advantage on dissimilarity data stemming from extended objects, manifold structures or a combination of these qualities. We also show that the induced sparsity during CR, is of great significance to the classification performance, especially in cases with small representative sets in the training data and large number of classes. © 2013 Springer-Verlag.