Information Processing & Algorithm Laboratory

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 propose new fast and accurate algorithms to solve the subproblems problems 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.

A implementation of LRSDL and other well-known dictionary learning methods can be found in [this Github repository].

The version of this toolbox. You can find it in [this Github repository].

Tiep H. Vu, Vishal Monga. "Fast Low-rank Shared Dictionary Learning for Image Classification." IEEE Transactions on Image Processing, volume 26, issue 11, pages 5160-5175, November 2017 [paper].
Tiep H. Vu, Vishal Monga. "Learning a low-rank shared dictionary for object classification." IEEE International Conference on Image Processing (ICIP), pp. 4428-4432, 2016. [ paper], [ICIP poster].

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The Sparse Modeling Software [project page].

Fast low-rank shared dictionary learning
for object classification

LRSDL

ABSTRACT

DICTOL - Dictionary learning toolbox

Motivation

Idea Visualization & Cost Function

Convergence Rate & Computational Complexity

Samples from Widely-used Datasets

Classification Results

Related Publications

Selected References

Fast low-rank shared dictionary learningfor object classification