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Randomized neural network based signature for color texture classification

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Abstract

Color texture analysis is an important subject in computer vision research. This paper presents an innovative and powerful color texture analysis method based on a randomized neural network. This approach uses the weights of the neural network as attributes for a color feature vector. Experiments were performed in three well-established benchmarks (Vistex, USPtex and Outex) and two rotated versions of these datasets (Vistex and Outex). The results were promising, surpassing the accuracies of most of the compared methods. This achievement allows us to affirm that the proposed approach is a valuable tool to be included in color texture analysis field.

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Acknowledgements

Jarbas Joaci de Mesquita Sá Junior thanks CNPq (National Council for Scientific and Technological Development, Brazil, Grant Nos. 152054/2016-2 and 302183/2017-5) for the financial support of this work. André R. Backes gratefully acknowledges the financial support of CNPq (Grant #302416/2015-3) and FAPEMIG (Foundation to the Support of Research in Minas Gerais, Grant #APQ-03437-15). Odemir M. Bruno gratefully acknowledges the financial support of CNPq (307797/2014-7) and FAPESP (14/08026-1 and 16/18809-9).

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Authors and Affiliations

  1. São Carlos Institute of Physics, University of São Paulo, PO Box 369, São Carlos, SP, 13560-970, Brazil

    Jarbas Joaci de Mesquita Sá Junior & Odemir Martinez Bruno

  2. Department of Computer Engineering, Federal University of Ceará - Campus Sobral, Rua Coronel Estanislau Frota, 563, Centro, Sobral, CE, 62010-560, Brazil

    Jarbas Joaci de Mesquita Sá Junior

  3. School of Computer Science, Federal University of Uberlândia, Av. João Naves de Ávila, 2121, Uberlândia, MG, 38408-100, Brazil

    André Ricardo Backes

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  1. Jarbas Joaci de Mesquita Sá Junior
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  2. André Ricardo Backes
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  3. Odemir Martinez Bruno
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Correspondence to Jarbas Joaci de Mesquita Sá Junior.

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Sá Junior, J.J.M., Backes, A.R. & Bruno, O.M. Randomized neural network based signature for color texture classification. Multidim Syst Sign Process 30, 1171–1186 (2019). https://doi.org/10.1007/s11045-018-0600-6

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