Local Jet Pattern: A Robust Descriptor for Texture Classification

TL;DR

The paper introduces Local Jet Pattern (LJP), a robust and efficient texture descriptor derived from Gaussian derivative responses, achieving high accuracy in texture classification despite variations in scale, illumination, and viewpoint.

Contribution

It proposes a novel local jet pattern descriptor based on Gaussian derivatives that is invariant to scale, rotation, and reflection, improving texture classification performance.

Findings

Achieves near-perfect accuracy on standard texture datasets.

Outperforms existing state-of-the-art texture classification methods.

Demonstrates robustness to scale, rotation, and illumination changes.

Abstract

Methods based on local image features have recently shown promise for texture classification tasks, especially in the presence of large intra-class variation due to illumination, scale, and viewpoint changes. Inspired by the theories of image structure analysis, this paper presents a simple, efficient, yet robust descriptor namely local jet pattern (LJP) for texture classification. In this approach, a jet space representation of a texture image is derived from a set of derivatives of Gaussian (DtGs) filter responses up to second order, so called local jet vectors (LJV), which also satisfy the Scale Space properties. The LJP is obtained by utilizing the relationship of center pixel with the local neighborhood information in jet space. Finally, the feature vector of a texture region is formed by concatenating the histogram of LJP for all elements of LJV. All DtGs responses up to second order together preserves the intrinsic local image structure, and achieves invariance to scale, rotation, and reflection. This allows us to develop a texture classification framework which is discriminative and robust. Extensive experiments on five standard texture image databases, employing nearest subspace classifier (NSC), the proposed descriptor achieves 100%, 99.92%, 99.75%, 99.16%, and 99.65% accuracy for Outex_TC-00010 (Outex_TC10), and Outex_TC-00012 (Outex_TC12), KTH-TIPS, Brodatz, CUReT, respectively, which are outperforms the state-of-the-art methods.