Analysis of single‑ and dual‑dictionary strategies in pedestrian classification

Abstract

Sparse coding has recently been a hot topic in visual tasks in image processing and computer vision. It has applications and brings benefts in reconstruction-like tasks and in classifcation-like tasks as well. However, regarding binary classifcation problems, there are several choices to learn and use dictionaries that have not been studied. In particular, how single-dictionary and dual-dictionary approaches compare in terms of classifcation performance is largely unexplored. We compare three single-dictionary strategies and two dual-dictionary strategies for the problem of pedestrian classifcation (“pedestrian” vs “background” images). In each of these fve cases, images are represented as the sparse coefcients induced from the respective dictionaries, and these coefcients are the input to a regular classifer both for training and subsequent classifcation of novel unseen instances. Experimental results with the INRIA pedestrian dataset suggest, on the one hand, that dictionaries learned from only one of the classes, even from the background class, are enough for obtaining competitive good classifcation performance. On the other hand, while better performance is generally obtained when instances of both classes are used for dictionary learning, the representation induced by a single dictionary learned from a set of instances from both classes provides comparable or even superior performance over the representations induced by two dictionaries learned separately from the pedestrian and background classes.