{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T04:46:09Z","timestamp":1771476369305,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T00:00:00Z","timestamp":1723593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T00:00:00Z","timestamp":1723593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"State Grid Corporation Science and Technology Project","award":["5700-202199539A-0-5-ZN"],"award-info":[{"award-number":["5700-202199539A-0-5-ZN"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s13042-024-02314-0","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T02:02:00Z","timestamp":1723600920000},"page":"947-958","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Multi-scale frequency domain learning for texture classification"],"prefix":"10.1007","volume":"16","author":[{"given":"Liguang","family":"Zang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8512-5475","authenticated-orcid":false,"given":"Yuancheng","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,14]]},"reference":[{"key":"2314_CR1","unstructured":"Alex K, Ilya S, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. NIPS 1106\u20131114"},{"key":"2314_CR2","first-page":"630","volume":"4","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. ECCV 4:630\u2013645","journal-title":"ECCV"},{"key":"2314_CR3","volume-title":"Network in network","author":"M Lin","year":"2014","unstructured":"Lin M, Chen Q, Yan S (2014) Network in network. ICLR"},{"key":"2314_CR4","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. CVPR, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"4","key":"2314_CR5","doi-asserted-by":"publisher","first-page":"4430","DOI":"10.1109\/TPAMI.2022.3194044","volume":"45","author":"H Li","year":"2023","unstructured":"Li H, Wang G, Wang B, Liang X, Li Z, Chang X (2023) DS-Net++: dynamic weight slicing for efficient inference in CNNs and vision transformers. IEEE Trans Pattern Anal Mach Intell 45(4):4430\u20134446","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2314_CR6","unstructured":"Karen S, Andrew Z (2015) Very deep convolutional networks for large-scale image recognition. ICLR"},{"issue":"8","key":"2314_CR7","doi-asserted-by":"publisher","first-page":"2685","DOI":"10.1007\/s13042-023-01791-z","volume":"14","author":"F An","year":"2023","unstructured":"An F, Wang J (2023) Target detection algorithm based on multilayer attention mechanism-adaptive feature fusion network. Int J Mach Learn Cybern 14(8):2685\u20132695","journal-title":"Int J Mach Learn Cybern"},{"key":"2314_CR8","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3245613","author":"J Li","year":"2023","unstructured":"Li J, Bi Y, Wang S, Li Q (2023) CFRLA-Net: a context-aware feature representation learning anchor-free network for pedestrian detection. IEEE Trans Circ Syst Video Technol. https:\/\/doi.org\/10.1109\/TCSVT.2023.3245613","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"2314_CR9","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. CVPR, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"2314_CR10","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick RB, Doll\u00e1r P, Tu Z, He K (2017) Aggregated residual transformations for deep neural networks. CVPR, pp 5987\u20135995","DOI":"10.1109\/CVPR.2017.634"},{"key":"2314_CR11","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3224068","author":"W Xiong","year":"2022","unstructured":"Xiong W, Xiong Z, Cui Y, Huang L, Yang R (2022) An interpretable fusion Siamese network for multi-modality remote sensing ship image retrieval. IEEE Trans Circ Syst Video Technol. https:\/\/doi.org\/10.1109\/TCSVT.2022.3224068","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"2314_CR12","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3233042","author":"W Li","year":"2022","unstructured":"Li W, Ma Z, Deng L-J, Fan X, Tian Y (2022) Neuron-based spiking transmission and reasoning network for robust image-text retrieval. IEEE Trans Circ Syst Video Technol. https:\/\/doi.org\/10.1109\/TCSVT.2022.3233042","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"2314_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3232634","author":"M Tang","year":"2022","unstructured":"Tang M, Wang Z, Zeng Z, Li X, Zhou L (2022) Stay in grid: improving video captioning via fully grid-level representation. IEEE Trans Circ Syst Video Technol. https:\/\/doi.org\/10.1109\/TCSVT.2022.3232634","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"2314_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3229079","author":"H Amirpour","year":"2022","unstructured":"Amirpour H, Ghanbari M, Timmerer C (2022) DeepStream: video streaming enhancements using compressed deep neural networks. IEEE Trans Circ Syst Video Technol. https:\/\/doi.org\/10.1109\/TCSVT.2022.3229079","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"2314_CR15","doi-asserted-by":"crossref","unstructured":"Xu K, Qin M, Sun F, Wang Y, Chen Y-K, Ren F (2020) Learning in the frequency domain. CVPR, pp 1737\u20131746","DOI":"10.1109\/CVPR42600.2020.00181"},{"key":"2314_CR16","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.cogsys.2018.07.015","volume":"52","author":"K Gopala Krishnan","year":"2018","unstructured":"Gopala Krishnan K, Vanathi PT (2018) An efficient texture classification algorithm using integrated Discrete Wavelet Transform and local binary pattern features. Cogn Syst Res 52:267\u2013274","journal-title":"Cogn Syst Res"},{"issue":"8","key":"2314_CR17","doi-asserted-by":"publisher","first-page":"2037","DOI":"10.1049\/ipr2.12466","volume":"16","author":"L Wang","year":"2022","unstructured":"Wang L, Sun Y (2022) Image classification using convolutional neural network with wavelet domain inputs. IET Image Process 16(8):2037\u20132048","journal-title":"IET Image Process"},{"issue":"1","key":"2314_CR18","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/s10044-020-00907-3","volume":"24","author":"S Nithya","year":"2021","unstructured":"Nithya S, Ramakrishnan S (2021) Wavelet domain majority coupled binary pattern: a new descriptor for texture classification. Pattern Anal Appl 24(1):393\u2013408","journal-title":"Pattern Anal Appl"},{"key":"2314_CR19","doi-asserted-by":"crossref","unstructured":"Chaorong L, Yuanyuan H (2017) Deep Decomposition of Circularly Symmetric Gabor Wavelet for rotation-invariant texture image classification. ICIP, pp 2702\u20132706","DOI":"10.1109\/ICIP.2017.8296773"},{"key":"2314_CR20","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.patrec.2015.07.038","volume":"65","author":"H Hadizadeh","year":"2015","unstructured":"Hadizadeh H (2015) Multi-resolution local Gabor wavelets binary patterns for gray-scale texture description. Pattern Recognit Lett 65:163\u2013169","journal-title":"Pattern Recognit Lett"},{"issue":"10","key":"2314_CR21","doi-asserted-by":"publisher","first-page":"2495","DOI":"10.1109\/TPAMI.2018.2857824","volume":"41","author":"Y Wang","year":"2019","unstructured":"Wang Y, Chang X, Chao X, Tao D (2019) Packing convolutional neural networks in the frequency domain. IEEE Trans Pattern Anal Mach Intell 41(10):2495\u20132510","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2314_CR22","unstructured":"Zhenhua L, Jizheng X, Xiulian P, Ruiqin X (2018) Frequency-domain dynamic pruning for convolutional neural networks. NeurIPS, pp 1051\u20131061"},{"key":"2314_CR23","unstructured":"Dziedzic A, Paparrizos J, Krishnan S, Elmore AJ, Franklin MJ (2019) Band-limited training and inference for convolutional neural networks. ICML, pp 1745\u20131754"},{"key":"2314_CR24","first-page":"328","volume":"25","author":"Y Ting","year":"2022","unstructured":"Ting Y, Yingwei P, Yehao L, Chong-Wah N, Mei T (2022) Wave-ViT: unifying wavelet and transformers for visual representation learning. ECCV 25:328\u2013345","journal-title":"ECCV"},{"key":"2314_CR25","unstructured":"Zequn Q, Pengyi Z, Fei W, Xi L (2021) FcaNet: frequency channel attention networks. ICCV, pp 763\u2013772"},{"key":"2314_CR26","doi-asserted-by":"crossref","unstructured":"Xinyu L, Yanyi Z, Jianbo Y, Hanlin L, Yibo Z (2023) Discrete Cosin TransFormer: image modeling from frequency domain. WACV, pp 5457\u20135467","DOI":"10.1109\/WACV56688.2023.00543"},{"key":"2314_CR27","doi-asserted-by":"crossref","unstructured":"Gopal L (2012) Suppressing first row\/column coefficients of DCT blocks for lossless JPEG code reduction. ICIP, pp 2469\u20132472","DOI":"10.1109\/ICIP.2012.6467398"},{"key":"2314_CR28","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. CVPR, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"2314_CR29","doi-asserted-by":"crossref","unstructured":"Christian S, Sergey I, Vincent V, Alexander AA (2017) Inception-v4, Inception-ResNet and the impact of residual connections on learning. AAAI: 4278\u20134284","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"2314_CR30","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, van der Maaten L, Kilian Q, Weinberger (2017) Densely connected convolutional networks. CVPR, pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"key":"2314_CR31","doi-asserted-by":"crossref","unstructured":"Cimpoi M, Maji S, Vedaldi A (2015) Deep filter banks for texture recognition and segmentation. CVPR, pp 3828\u20133836","DOI":"10.1109\/CVPR.2015.7299007"},{"key":"2314_CR32","doi-asserted-by":"crossref","unstructured":"Tsung-Yu L, Subhransu M (2016) Visualizing and understanding deep texture representations. CVPR, pp 2791\u20132799","DOI":"10.1109\/CVPR.2016.305"},{"key":"2314_CR33","doi-asserted-by":"publisher","first-page":"8776","DOI":"10.1109\/TIP.2020.3019185","volume":"29","author":"X Dong","year":"2020","unstructured":"Dong X, Zhou H, Dong J (2020) Texture classification using pair-wise difference pooling-based bilinear convolutional neural networks. IEEE Trans Image Process 29:8776\u20138790","journal-title":"IEEE Trans Image Process"},{"issue":"8","key":"2314_CR34","doi-asserted-by":"publisher","first-page":"3910","DOI":"10.1109\/TIP.2019.2903300","volume":"28","author":"L Liu","year":"2019","unstructured":"Liu L, Chen J, Zhao G, Fieguth PW, Chen X, Pietik\u00e4inen M (2019) Texture classification in extreme scale variations using GANet. IEEE Trans Image Process 28(8):3910\u20133922","journal-title":"IEEE Trans Image Process"},{"key":"2314_CR35","unstructured":"Ken C, Karen S, Andrea V, Andrew Z (2014) Return of the devil in the details: delving deep into convolutional nets. BMVC"},{"key":"2314_CR36","doi-asserted-by":"crossref","unstructured":"Hang Z, Jia X, Kristin JD (2017) Deep TEN: texture encoding network. CVPR, pp 2896\u20132905","DOI":"10.1109\/CVPR.2017.309"},{"key":"2314_CR37","unstructured":"Rippel O, Snoek J, Adams RP (2015) Spectral representations for convolutional neural networks. In Neural Information Processing Systems (NIPS), pages 2449\u20132457, 4"},{"issue":"11","key":"2314_CR38","doi-asserted-by":"publisher","first-page":"1549","DOI":"10.1109\/83.469936","volume":"4","author":"M Unser","year":"1995","unstructured":"Unser M (1995) Texture classification and segmentation using wavelet frames. IEEE Trans Image Process 4(11):1549\u20131560 (1, 2)","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"2314_CR39","first-page":"194","volume":"2","author":"M Kanchana","year":"2012","unstructured":"Kanchana M, Varalakshmi P (2012) Texture classification using discrete Shearlet transform. Int J Sci Res 2(6):194\u2013196","journal-title":"Int J Sci Res"},{"key":"2314_CR40","doi-asserted-by":"publisher","unstructured":"Fujieda S, Takayama K, Hachisuka T (2018) Wavelet convolutional neural networks. https:\/\/doi.org\/10.48550\/arXiv.1805.08620","DOI":"10.48550\/arXiv.1805.08620"},{"issue":"3","key":"2314_CR41","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1142\/S0219691307001847","volume":"5","author":"S Arivazhagan","year":"2007","unstructured":"Arivazhagan S, Subash Kumar TG, Ganesan L (2007) Texture classification using curvelet transform. Int J Wavelets Multiresolution Inf Process 5(3):451\u2013464","journal-title":"Int J Wavelets Multiresolution Inf Process"},{"key":"2314_CR42","unstructured":"Max E, Larry D (2019) Deep residual learning in the JPEG transform domain. ICCV, pp 3483\u20133492"},{"issue":"12","key":"2314_CR43","doi-asserted-by":"publisher","first-page":"2771","DOI":"10.1007\/s13042-020-01150-2","volume":"11","author":"A Wang","year":"2020","unstructured":"Wang A, Jin Z, Yang J (2020) A faster tensor robust PCA via tensor factorization. Int J Mach Learn Cybern 11(12):2771\u20132791","journal-title":"Int J Mach Learn Cybern"},{"key":"2314_CR44","doi-asserted-by":"crossref","unstructured":"Eric H, Barbara C, Mario F, Eklundh J-O (2004) On the significance of real-world conditions for material classification. ECCV (4):253\u2013266","DOI":"10.1007\/978-3-540-24673-2_21"},{"key":"2314_CR45","doi-asserted-by":"crossref","unstructured":"Cimpoi M, Maji S, Kokkinos I, Mohamed S, Vedaldi A (2014) Describing textures in the wild. CVPR, pp 3606\u20133613","DOI":"10.1109\/CVPR.2014.461"},{"issue":"8","key":"2314_CR46","doi-asserted-by":"publisher","first-page":"784","DOI":"10.1167\/9.8.784","volume":"9","author":"L Sharan","year":"2012","unstructured":"Sharan L, Rosenholtz R, Adelson EH (2012) Material perception: What can you see in a brief glance? J Vis 9(8):784\u2013784","journal-title":"J Vis"},{"key":"2314_CR47","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.patrec.2016.08.016","volume":"84","author":"V Andrearczyk","year":"2016","unstructured":"Andrearczyk V, Whelan PF (2016) Using filter banks in Convolutional Neural Networks for texture classification. Pattern Recogn Lett 84:63\u201369","journal-title":"Pattern Recogn Lett"},{"key":"2314_CR48","doi-asserted-by":"crossref","unstructured":"Hafemann LG, Oliveira LS, Cavalin PR (2014) Forest species recognition using deep convolutional neural networks. ICPR, pp 1103\u20131107","DOI":"10.1109\/ICPR.2014.199"},{"key":"2314_CR49","doi-asserted-by":"crossref","unstructured":"Krishnan KG, Vanathi PT, Abinaya R (2016) Performance analysis of texture classification techniques using shearlet transform. In: 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) IEEE","DOI":"10.1109\/WiSPNET.2016.7566368"},{"key":"2314_CR50","doi-asserted-by":"crossref","unstructured":"Qilong W, Banggu W, Pengfei Z, Peihua L, Wangmeng Z, Qinghua HH (2020) ECA-Net: efficient channel attention for deep convolutional neural networks. CVPR, pp 11531\u201311539","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"2314_CR51","unstructured":"Jia D, Wei D, Richard S, Li-Jia L, Kai L, Li F-F (2009) ImageNet: a large-scale hierarchical image database. CVPR, pp 248\u2013255"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02314-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02314-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02314-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T10:41:10Z","timestamp":1738665670000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02314-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,14]]},"references-count":51,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["2314"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02314-0","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,14]]},"assertion":[{"value":"28 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}