Proposals for local basis selection for the sparse representation-based classifier

In the domains of pattern recognition and computer vision, sparse representation classifier and its variants are considered as powerful classifiers. However, due to the use of sparse coding in most of its variants, classifying test samples is computationally expensive. Thus, it is not practical for scenarios demanding fast classification. For this reason, a two-phase coding classifier based on classic regularized least square was proposed recently. A significant limitation of this classifier is the fact that the number of local bases that should be handed over to the next coding phase should be specified manually. This paper overcomes this main limitation and proposes three data-driven schemes allowing an automatic estimation of the optimal size of the local bases. Experiments conducted on five image datasets show that the introduced schemes, despite their simplicity, can improve the performance of the two-phase linear coding classifier adopting ad hoc choices for the number of local bases.