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

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Abstract

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.

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Authors and Affiliations

  1. Federal Institute of Education, Science and Technology of Espírito Santo, Rodovia Governador José Sete, no̱ 184 - Itacibá, CEP 29150-410, Cariacica, ES, Brasil

    Pedro M. Silva

  2. Institute of Mathematics, Statistics and Scientific Computing - University of Campinas, Rua Sérgio Buarque de Holanda, 651, Cidade Universitária “Zeferino Vaz” - Distr. Barão Geraldo, CEP 13083-859, Campinas, SP, Brasil

    Joao B. Florindo

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  1. Pedro M. Silva
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Correspondence to Joao B. Florindo.

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J. B. F. gratefully acknowledges the financial support of São Paulo Research Foundation (FAPESP) (Grant #2016/16060-0) and from National Council for Scientific and Technological Development, Brazil (CNPq) (Grants #301480/2016-8 and #423292/2018-8).

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Silva, P.M., Florindo, J.B. Fractal measures of image local features: an application to texture recognition. Multimed Tools Appl 80, 14213–14229 (2021). https://doi.org/10.1007/s11042-020-10369-8

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  • DOI: https://doi.org/10.1007/s11042-020-10369-8

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