Multiscale Analysis for Improving Texture Classification

TL;DR

This paper introduces a multiscale texture analysis method using Gaussian-Laplacian pyramids combined with diverse feature descriptors, significantly enhancing texture classification accuracy over existing approaches.

Contribution

The paper proposes a novel multiscale feature aggregation technique that integrates multiple descriptors from different spatial frequency bands for improved texture classification.

Findings

Enhanced classification accuracy on texture datasets

Multiscale feature aggregation outperforms single-scale methods

Descriptors are complementary and improve texture characterization

Abstract

Information from an image occurs over multiple and distinct spatial scales. Image pyramid multiresolution representations are a useful data structure for image analysis and manipulation over a spectrum of spatial scales. This paper employs the Gaussian-Laplacian pyramid to treat different spatial frequency bands of a texture separately. First, we generate three images corresponding to three levels of the Gaussian-Laplacian pyramid for an input image to capture intrinsic details. Then we aggregate features extracted from gray and color texture images using bio-inspired texture descriptors, information-theoretic measures, gray-level co-occurrence matrix features, and Haralick statistical features into a single feature vector. Such an aggregation aims at producing features that characterize textures to their maximum extent, unlike employing each descriptor separately, which may lose some relevant textural information and reduce the classification performance. The experimental results on texture and histopathologic image datasets have shown the advantages of the proposed method compared to state-of-the-art approaches. Such findings emphasize the importance of multiscale image analysis and corroborate that the descriptors mentioned above are complementary.