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