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Pyramidal neural networks with evolved variable receptive fields

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Abstract

Pyramidal neural networks (PNN) are computational systems inspired by the concept of receptive fields from the human visual system. These neural networks are designed for implicit feature extraction and have been applied in pattern recognition applications. In the original approach, the size of the receptive field within the same 2D layer is a constant parameter, while in the human visual system, the receptive field size is variable. This paper proposes a PNN with variable receptive fields determined by an evolutionary algorithm, called variable pyramidal neural network with evolutionary algorithms. We observed from experiments aiming at detecting faces in images that our approach can achieve better classification rates than the original PNN. We also observed that regions with more information (such as nose and eyes) are more emphasized by variable receptive fields. These results confirm the application of intelligent algorithms to determine adjustable receptive fields in neural networks is useful to find out relevant information for recognition task. Besides, the model is comparable to biological systems regarding the flexibility assigned to receptive fields.

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Adapted from Fernandes et al. [4]

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Acknowledgements

This research was partially supported by Brazilian agencies Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento Pessoal de Nível Superior (CAPES) and Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (FACEPE).

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Authors and Affiliations

  1. Escola Politécnica at Universidade de Pernambuco, Recife, Brazil

    Alessandra M. Soares, Bruno J. T. Fernandes & Carmelo J. A. Bastos-Filho

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  1. Alessandra M. Soares
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  2. Bruno J. T. Fernandes
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  3. Carmelo J. A. Bastos-Filho
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Correspondence to Bruno J. T. Fernandes.

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Soares, A.M., Fernandes, B.J.T. & Bastos-Filho, C.J.A. Pyramidal neural networks with evolved variable receptive fields. Neural Comput & Applic 29, 1443–1453 (2018). https://doi.org/10.1007/s00521-016-2656-2

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  • DOI: https://doi.org/10.1007/s00521-016-2656-2

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