Fast Low-rank Shared Dictionary Learning for Image Classification

TL;DR

This paper introduces a fast, low-rank shared dictionary learning method that captures both common and class-specific features for improved image classification, with algorithms that are faster and more accurate than existing approaches.

Contribution

It proposes a novel joint learning framework with a low-rank shared dictionary and class-specific dictionaries, along with efficient algorithms for faster convergence.

Findings

Outperforms state-of-the-art dictionary learning methods on image datasets.

Algorithms are faster and more accurate than existing methods.

The low-rank constraint effectively captures shared patterns across classes.

Abstract

Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. This observation has been exploited partially in a recently proposed dictionary learning framework by separating the particularity and the commonality (COPAR). Inspired by this, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification with more intuitive constraints. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries. For the shared dictionary, we enforce a low-rank constraint, i.e. claim that its spanning subspace should have low dimension and the coefficients corresponding to this dictionary should be similar. For the particular dictionaries, we impose on them the well-known constraints stated in the Fisher discrimination dictionary learning (FDDL). Further, we develop new fast and accurate algorithms to solve the subproblems in the learning step, accelerating its convergence. The said algorithms could also be applied to FDDL and its extensions. The efficiencies of these algorithms are theoretically and experimentally verified by comparing their complexities and running time with those of other well-known dictionary learning methods. Experimental results on widely used image datasets establish the advantages of our method over state-of-the-art dictionary learning methods.