# Fractal measures of image local features: an application to texture recognition

arXiv: https://arxiv.org/abs/2108.12491 · HTML: https://mlsanity.com/paper/2108.12491/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 49 references that are in the map.
- First posted on arXiv 27 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [PCANet: A Simple Deep Learning Baseline for Image Classification?](https://mlsanity.com/paper/1404.3606/) (2014)
- [Deep filter banks for texture recognition, description, and segmentation](https://mlsanity.com/paper/1507.02620/) (2015)
- [Discrete Schroedinger Transform For Texture Recognition](https://mlsanity.com/paper/1612.02498/) (2016)
- [Enhancing fractal descriptors on images by combining boundary and interior of Minkowski dilation](https://mlsanity.com/paper/1412.7880/) (2014)

## Built on by (most cited first, 3 of 3)

- [Multilayer deep feature extraction for visual texture recognition](https://mlsanity.com/paper/2208.10044/) (2022)
- [Distinctive Self-Similar Object Detection](https://mlsanity.com/paper/2211.10995/) (2022)
- [Chaotic Contrastive Learning for Robust Texture Classification](https://mlsanity.com/paper/2605.05012/) (2026)

## Abstract (arXiv)

Here we propose a new method for the classification of texture images combining fractal measures (fractal dimension, multifractal spectrum and lacunarity) with local binary patterns. More specifically we compute the box counting dimension of the local binary codes thresholded at different levels to compose the feature vector. The proposal is assessed in the classification of three benchmark databases: KTHTIPS-2b, UMD and UIUC as well as in a real-world problem, namely the identification of Brazilian plant species (database 1200Tex) using scanned images of their leaves. The proposed method demonstrated to be competitive with other state-of-the-art solutions reported in the literature. Such results confirmed the potential of combining a powerful local coding description with the multiscale information captured by the fractal dimension for texture classification.