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Proceedings Paper
Volume: 4 | Article ID: 26
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Optimizing Gabor Texture Features for Materials Recognition by Convolutional Neural Networks
  DOI :  10.2352/lim.2023.4.1.28  Published OnlineJune 2023
Abstract
Abstract

In this paper, we present a novel technique that allows for customized Gabor texture features by leveraging deep learning neural networks. Our method involves using a Convolutional Neural Network to refactor traditional, hand-designed filters on specific datasets. The refactored filters can be used in an off-the-shelf manner with the same computational cost but significantly improved accuracy for material recognition. We demonstrate the effectiveness of our approach by reporting a gain in discriminatio accuracy on different material datasets. Our technique is particularly appealing in situations where the use of the entire CNN would be inadequate, such as analyzing non-square images or performing segmentation tasks. Overall, our approach provides a powerful tool for improving the accuracy of material recognition tasks while retaining the advantages of handcrafted filters.

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Francesco Bianconi, Claudio Cusano, Paolo Napoletano, Raimondo Schettini, "Optimizing Gabor Texture Features for Materials Recognition by Convolutional Neural Networksin London Imaging Meeting,  2023,  pp 118 - 121,  https://doi.org/10.2352/lim.2023.4.1.28

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