KCRC-LCD: Discriminative Kernel Collaborative Representation with Locality Constrained Dictionary for Visual Categorization

TL;DR

This paper introduces KCRC-LCD, a novel image classification method combining kernel collaborative representation with a locality constrained dictionary, improving discrimination and scalability over traditional CRC methods.

Contribution

The paper proposes a new kernel-based collaborative classification framework with a locality constrained dictionary, enhancing discrimination and scalability in image recognition tasks.

Findings

KCRC-LCD outperforms traditional CRC in classification accuracy.

The locality constrained dictionary improves scalability to large datasets.

The unified similarity measure enhances the discrimination ability of the model.

Abstract

We consider the image classification problem via kernel collaborative representation classification with locality constrained dictionary (KCRC-LCD). Specifically, we propose a kernel collaborative representation classification (KCRC) approach in which kernel method is used to improve the discrimination ability of collaborative representation classification (CRC). We then measure the similarities between the query and atoms in the global dictionary in order to construct a locality constrained dictionary (LCD) for KCRC. In addition, we discuss several similarity measure approaches in LCD and further present a simple yet effective unified similarity measure whose superiority is validated in experiments. There are several appealing aspects associated with LCD. First, LCD can be nicely incorporated under the framework of KCRC. The LCD similarity measure can be kernelized under KCRC, which theoretically links CRC and LCD under the kernel method. Second, KCRC-LCD becomes more scalable to both the training set size and the feature dimension. Example shows that KCRC is able to perfectly classify data with certain distribution, while conventional CRC fails completely. Comprehensive experiments on many public datasets also show that KCRC-LCD is a robust discriminative classifier with both excellent performance and good scalability, being comparable or outperforming many other state-of-the-art approaches.