# Multiscale Analysis for Improving Texture Classification

arXiv: https://arxiv.org/abs/2204.09841 · HTML: https://mlsanity.com/paper/2204.09841/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 21 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [From BoW to CNN: Two Decades of Texture Representation for Texture Classification](https://mlsanity.com/paper/1801.10324/) (2018)
- [Wavelet Convolutional Neural Networks for Texture Classification](https://mlsanity.com/paper/1707.07394/) (2017)
- [Using Filter Banks in Convolutional Neural Networks for Texture Classification](https://mlsanity.com/paper/1601.02919/) (2016)
- [Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network](https://mlsanity.com/paper/1811.04241/) (2018)
- [Machine Learning Methods for Histopathological Image Analysis: A Review](https://mlsanity.com/paper/2102.03889/) (2021)
- [Data Augmentation for Histopathological Images Based on Gaussian-Laplacian Pyramid Blending](https://mlsanity.com/paper/2002.00072/) (2020)
- [Texture CNN for Histopathological Image Classification](https://mlsanity.com/paper/1905.12005/) (2019)
- [A Novel Bio-Inspired Texture Descriptor based on Biodiversity and Taxonomic Measures](https://mlsanity.com/paper/2102.06997/) (2021)
- [Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform](https://mlsanity.com/paper/2202.13270/) (2022)

## Abstract (arXiv)

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.