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Texture descriptor combining fractal dimension and artificial crawlers

Author

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  • Gonçalves, Wesley Nunes
  • Machado, Bruno Brandoli
  • Bruno, Odemir Martinez

Abstract

Texture is an important visual attribute used to describe images. There are many methods available for texture analysis. However, they do not capture the detail richness of the image surface. In this paper, we propose a new method to describe textures using the artificial crawler model. This model assumes that agents can interact with the environment and each other. Since this swarm system alone does not achieve a good discrimination, we developed a new method to increase the discriminatory power of artificial crawlers, together with the fractal dimension theory. Here, we estimated the fractal dimension by the Bouligand–Minkowski method due to its precision in quantifying structural properties of images. We validate our method on two texture datasets and the experimental results reveal that our method leads to highly discriminative textural features. The results indicate that our method can be used in different texture applications.

Suggested Citation

  • Gonçalves, Wesley Nunes & Machado, Bruno Brandoli & Bruno, Odemir Martinez, 2014. "Texture descriptor combining fractal dimension and artificial crawlers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 358-370.
    • Handle: RePEc:eee:phsmap:v:395:y:2014:i:c:p:358-370
    DOI: 10.1016/j.physa.2013.10.011
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    References listed on IDEAS

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    1. Florindo, João B. & Sikora, Mariana S. & Pereira, Ernesto C. & Bruno, Odemir M., 2013. "Characterization of nanostructured material images using fractal descriptors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(7), pages 1694-1701.
    2. Florindo, João Batista & Bruno, Odemir Martinez, 2012. "Fractal descriptors based on Fourier spectrum applied to texture analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4909-4922.
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    Citations

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    Cited by:

    1. Zunino, Luciano & Ribeiro, Haroldo V., 2016. "Discriminating image textures with the multiscale two-dimensional complexity-entropy causality plane," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 679-688.
    2. Lahmiri, Salim, 2016. "Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 235-243.

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