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Computer Science > Computer Vision and Pattern Recognition

arXiv:2108.12491 (cs)
[Submitted on 27 Aug 2021]

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

Authors:Pedro M. Silva, Joao B. Florindo
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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.
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2108.12491 [cs.CV]
  (or arXiv:2108.12491v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2108.12491

Submission history

From: Joao Florindo [view email]
[v1] Fri, 27 Aug 2021 20:27:28 UTC (174 KB)
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