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Adaptive-scale convolutional neural networks for texture image analysis

Adaptive-scale convolutional neural networks for texture image analysis This paper proposes an effective adaptive-scale convolutional neural networks (A-SCNN) for texture image analysis. We combine the multi-scale texture image analysis with the efficient feature space of a convolutional neural network to extract characteristic texture features. These latter encode regions of adaptive sizes centered on each pixel according to different optimal scales reflecting the local structure pattern content. To fix the scale-space values accurately, the Hessian-Laplacian operator is used. Experimental results demonstrate a good performance of the proposed A-SCNN in texture classification. Particularly, the CNN based on the adaptive scale shows promising for irregular texture pattern classification, and the selective sizes of both feature maps and receptive fields can further improve the performance of the classical CNN texture discrimination ability. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Signal and Imaging Systems Engineering Inderscience Publishers

Adaptive-scale convolutional neural networks for texture image analysis

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1748-0698
eISSN
1748-0701
DOI
10.1504/IJSISE.2017.087767
Publisher site
See Article on Publisher Site

Abstract

This paper proposes an effective adaptive-scale convolutional neural networks (A-SCNN) for texture image analysis. We combine the multi-scale texture image analysis with the efficient feature space of a convolutional neural network to extract characteristic texture features. These latter encode regions of adaptive sizes centered on each pixel according to different optimal scales reflecting the local structure pattern content. To fix the scale-space values accurately, the Hessian-Laplacian operator is used. Experimental results demonstrate a good performance of the proposed A-SCNN in texture classification. Particularly, the CNN based on the adaptive scale shows promising for irregular texture pattern classification, and the selective sizes of both feature maps and receptive fields can further improve the performance of the classical CNN texture discrimination ability.

Journal

International Journal of Signal and Imaging Systems Engineering – Inderscience Publishers

Published: Jan 1, 2017

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