Label Embedded Dictionary Learning for Image Classification

TL;DR

This paper introduces a label embedded dictionary learning method using L1-norm regularization for image classification, improving optimization and achieving superior results over traditional algorithms.

Contribution

The proposed LEDL method replaces L0-norm with L1-norm regularization, enabling convex optimization and better classification performance.

Findings

Achieved superior accuracy on six benchmark datasets.

Effectively avoids NP-hard optimization problems.

Outperforms conventional classification algorithms.

Abstract

Recently, label consistent k-svd (LC-KSVD) algorithm has been successfully applied in image classification. The objective function of LC-KSVD is consisted of reconstruction error, classification error and discriminative sparse codes error with L0-norm sparse regularization term. The L0-norm, however, leads to NP-hard problem. Despite some methods such as orthogonal matching pursuit can help solve this problem to some extent, it is quite difficult to find the optimum sparse solution. To overcome this limitation, we propose a label embedded dictionary learning (LEDL) method to utilise the L1-norm as the sparse regularization term so that we can avoid the hard-to-optimize problem by solving the convex optimization problem. Alternating direction method of multipliers and blockwise coordinate descent algorithm are then exploited to optimize the corresponding objective function. Extensive experimental results on six benchmark datasets illustrate that the proposed algorithm has achieved superior performance compared to some conventional classification algorithms.