Linear Spatial Pyramid Matching Using Non-convex and non-negative Sparse Coding for Image Classification

TL;DR

This paper introduces an enhanced sparse coding approach for image classification that combines non-convex and non-negative constraints within a linear spatial pyramid matching framework, improving performance on standard datasets.

Contribution

It proposes a novel sparse coding model with non-convex and non-negative properties integrated into spatial pyramid matching for better image classification results.

Findings

Outperforms original ScSPM on multiple datasets

Demonstrates the effectiveness of non-convex and non-negative constraints

Improves image classification accuracy

Abstract

Recently sparse coding have been highly successful in image classification mainly due to its capability of incorporating the sparsity of image representation. In this paper, we propose an improved sparse coding model based on linear spatial pyramid matching(SPM) and Scale Invariant Feature Transform (SIFT ) descriptors. The novelty is the simultaneous non-convex and non-negative characters added to the sparse coding model. Our numerical experiments show that the improved approach using non-convex and non-negative sparse coding is superior than the original ScSPM[1] on several typical databases.