{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T15:55:07Z","timestamp":1787759707196,"version":"build-2784847793"},"reference-count":34,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T00:00:00Z","timestamp":1711065600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Textures provide a powerful segmentation and object detection cue. Recent research has shown that deep convolutional nets like Visual Geometry Group (VGG) and ResNet perform well in non-stationary texture datasets. Non-stationary textures have local structures that change from one region of the image to the other. This is consistent with the view that deep convolutional networks are good at detecting local microstructures disguised as textures. However, stationary textures are textures that have statistical properties that are constant or slow varying over the entire region are not well detected by deep convolutional networks. This research demonstrates that simple seven-layer convolutional networks can obtain better results than deep networks using a novel convolutional technique called orthogonal convolution with pre-calculated regional features using grey level co-occurrence matrix. We obtained an average of 8.5% improvement in accuracy in texture recognition on the Outex dataset over GoogleNet, ResNet, VGG and AlexNet.<\/jats:p>","DOI":"10.7717\/peerj-cs.1927","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T09:46:01Z","timestamp":1711100761000},"page":"e1927","source":"Crossref","is-referenced-by-count":4,"title":["A novel approach to texture recognition combining deep learning orthogonal convolution with regional input features"],"prefix":"10.7717","volume":"10","author":[{"given":"Kar-Seng","family":"Loke","sequence":"first","affiliation":[{"name":"Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"issue":"C","key":"10.7717\/peerj-cs.1927\/ref-1","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.patrec.2016.08.016","article-title":"Using filter banks in Convolutional Neural Networks for texture classification","volume":"84","author":"Andrearczyk","year":"2016","journal-title":"Pattern Recognition Letters"},{"key":"10.7717\/peerj-cs.1927\/ref-2","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2016.572","article-title":"NetVLAD: CNN architecture for weakly supervised place recognition","author":"Arandjelovic","year":"2016"},{"key":"10.7717\/peerj-cs.1927\/ref-3","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.neunet.2017.10.001","article-title":"Deep neural networks for texture classification\u2014a theoretical analysis","volume":"97","author":"Basu","year":"2018","journal-title":"Neural Networks"},{"issue":"4","key":"10.7717\/peerj-cs.1927\/ref-4","doi-asserted-by":"publisher","first-page":"738","DOI":"10.3390\/app9040738","article-title":"Comparative evaluation of hand-crafted image descriptors vs. off-the-shelf CNN-based features for colour texture classification under ideal and realistic conditions","volume":"9","author":"Bello-Cerezo","year":"2019","journal-title":"Applied Sciences"},{"key":"10.7717\/peerj-cs.1927\/ref-5","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s11263-015-0872-3","article-title":"Deep filter banks for texture recognition, description, and segmentation","volume":"118","author":"Cimpoi","year":"2016","journal-title":"International Journal of Computer Vision"},{"key":"10.7717\/peerj-cs.1927\/ref-6","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2015.7299007","article-title":"Deep filter banks for texture recognition and segmentation","author":"Cimpoi","year":"2015"},{"key":"10.7717\/peerj-cs.1927\/ref-7","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1038\/nn.3402","article-title":"A functional and perceptual signature of the second visual area in primates","volume":"16","author":"Freeman","year":"2013","journal-title":"Nature Neuroscience"},{"key":"10.7717\/peerj-cs.1927\/ref-8","article-title":"ImageNet-Trained CNNs are biased towards texture; 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