Discriminative Bayesian Dictionary Learning for Classification

TL;DR

This paper introduces a Bayesian method for learning discriminative dictionaries for sparse data representation, which automatically determines the optimal dictionary size and improves classification accuracy across various datasets.

Contribution

It presents a novel Bayesian framework using a Beta Process for discriminative dictionary learning with automatic size inference and integrated classification.

Findings

Outperforms state-of-the-art methods in face and action recognition.

Automatically infers the optimal dictionary size.

Achieves superior classification accuracy on multiple datasets.

Abstract

We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.