{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:38:14Z","timestamp":1775745494986,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T00:00:00Z","timestamp":1620259200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T00:00:00Z","timestamp":1620259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672439"],"award-info":[{"award-number":["61672439"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["20720181004"],"award-info":[{"award-number":["20720181004"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s00521-021-06081-9","type":"journal-article","created":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T09:03:27Z","timestamp":1620291807000},"page":"14413-14428","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Automatic identification of commodity label images using lightweight attention network"],"prefix":"10.1007","volume":"33","author":[{"given":"Junde","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adnan","family":"Zeb","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuangyuan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2396-1205","authenticated-orcid":false,"given":"Defu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Y. A.","family":"Nanehkaran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,6]]},"reference":[{"key":"6081_CR1","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.jvcir.2018.12.049","volume":"59","author":"Y Wang","year":"2019","unstructured":"Wang Y, Wang Z (2019) A survey of recent work on fine-grained image classification techniques. J Vis Commun Image Represent 59:210\u2013214","journal-title":"J Vis Commun Image Represent"},{"issue":"1","key":"6081_CR2","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/s00521-016-2388-3","volume":"28","author":"SL Gomes","year":"2017","unstructured":"Gomes SL et al (2017) Embedded real-time speed limit sign recognition using image processing and machine learning techniques. Neural Comput Appl 28(1):573\u2013584","journal-title":"Neural Comput Appl"},{"key":"6081_CR3","doi-asserted-by":"publisher","first-page":"1945","DOI":"10.4028\/www.scientific.net\/AMR.694-697.1945","volume":"694","author":"CH Li","year":"2013","unstructured":"Li CH et al (2013) Algorithm research of two-dimensional size measurement on parts based on machine vision. Adv Mater Res 694:1945\u20131948","journal-title":"Adv Mater Res"},{"key":"6081_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2202\/1556-3758.1129","volume":"3","author":"V G\u00f6kmen","year":"2007","unstructured":"G\u00f6kmen V, S\u00fcg\u00fct I (2007) A non-contact computer vision based analysis of color in foods. Int J Food Eng 3:1\u201313","journal-title":"Int J Food Eng"},{"key":"6081_CR5","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.cviu.2018.09.001","volume":"176","author":"G Ciocca","year":"2018","unstructured":"Ciocca G, Napoletano P, Schettini R (2018) CNN-based features for retrieval and classification of food images. Comput Vis Image Underst 176:70\u201377","journal-title":"Comput Vis Image Underst"},{"key":"6081_CR6","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.compeleceng.2019.04.011","volume":"76","author":"G Geetharamani","year":"2019","unstructured":"Geetharamani G, Pandian A (2019) Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput Electric Eng 76:323\u2013338","journal-title":"Comput Electric Eng"},{"issue":"12","key":"6081_CR7","doi-asserted-by":"publisher","first-page":"8887","DOI":"10.1007\/s00521-019-04228-3","volume":"31","author":"RA Priyadharshini","year":"2019","unstructured":"Priyadharshini RA et al (2019) Maize leaf disease classification using deep convolutional neural networks. Neural Comput Appl 31(12):8887\u20138895","journal-title":"Neural Comput Appl"},{"key":"6081_CR8","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.compbiomed.2016.11.003","volume":"80","author":"Y Miki","year":"2017","unstructured":"Miki Y et al (2017) Classification of teeth in cone-beam CT using deep convolutional neural network. Comput Biol Med 80:24\u201329","journal-title":"Comput Biol Med"},{"key":"6081_CR9","doi-asserted-by":"publisher","first-page":"105475","DOI":"10.1016\/j.cmpb.2020.105475","volume":"193","author":"A Mahbod","year":"2020","unstructured":"Mahbod A et al (2020) (2020) Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification. Comput Methods Programs Biomed 193:105475","journal-title":"Comput Methods Programs Biomed"},{"key":"6081_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2016.11.031","volume":"230","author":"S Mondal","year":"2017","unstructured":"Mondal S, Bours P (2017) A study on continuous authentication using a combination of keystroke and mouse biometrics. Neurocomputing 230:1\u201322","journal-title":"Neurocomputing"},{"key":"6081_CR11","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.patcog.2016.11.015","volume":"64","author":"Y Duan","year":"2017","unstructured":"Duan Y et al (2017) SAR Image segmentation based on convolutional-wavelet neural network and markov random field. Pattern Recogn 64:255\u2013267","journal-title":"Pattern Recogn"},{"key":"6081_CR12","doi-asserted-by":"publisher","first-page":"4625","DOI":"10.1109\/TIM.2020.2983531","volume":"69","author":"R-E Precup","year":"2020","unstructured":"Precup R-E et al (2020) Evolving fuzzy models for prosthetic hand myoelectric-based control. IEEE Trans Instrum Meas 69:4625\u20134636","journal-title":"IEEE Trans Instrum Meas"},{"key":"6081_CR13","doi-asserted-by":"publisher","first-page":"105950","DOI":"10.1016\/j.asoc.2019.105950","volume":"86","author":"X Li","year":"2020","unstructured":"Li X et al (2020) Fault diagnostics between different type of components: a transfer learning approach. Appl Soft Comput 86:105950","journal-title":"Appl Soft Comput"},{"key":"6081_CR14","doi-asserted-by":"crossref","unstructured":"Ma J et al. (2019) Machine learning based cross-border E-commerce commodity customs product name recognition algorithm. In: Pacific Rim International Conference on Artificial Intelligence. Springer, Cham, pp 247\u2013256","DOI":"10.1007\/978-3-030-29894-4_19"},{"issue":"2","key":"6081_CR15","first-page":"154","volume":"17","author":"MU Ahmed","year":"2019","unstructured":"Ahmed MU et al (2019) A machine learning approach to classify pedestrians\u2019 events based on IMU and GPS. Int J Artif Intell 17(2):154\u2013167","journal-title":"Int J Artif Intell"},{"key":"6081_CR16","unstructured":"Zhang T, Chen E (2019) Product recognition algorithm based on HOG and bag of words model. In: 2019 8th International Symposium on Next Generation Electronics (ISNE), pp 1\u20133. IEEE"},{"issue":"5","key":"6081_CR17","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","volume":"14","author":"N Kussul","year":"2017","unstructured":"Kussul N et al (2017) Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci Remote Sens Lett 14(5):778\u2013782","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"6081_CR18","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inf Process Syst"},{"key":"6081_CR19","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.neucom.2018.09.081","volume":"323","author":"Y Ji","year":"2019","unstructured":"Ji Y et al (2019) Graph model-based salient object detection using objectness and multiple saliency cues. Neurocomputing 323:188\u2013202","journal-title":"Neurocomputing"},{"issue":"10","key":"6081_CR20","doi-asserted-by":"publisher","first-page":"5633","DOI":"10.1007\/s00521-019-04311-9","volume":"32","author":"X Zou","year":"2020","unstructured":"Zou X et al (2020) Multi-task cascade deep convolutional neural networks for large-scale commodity recognition. Neural Comput Appl 32(10):5633\u20135647","journal-title":"Neural Comput Appl"},{"key":"6081_CR21","doi-asserted-by":"crossref","unstructured":"Chen C, Yang R, Wang C (2017) Research and realization of commodity image retrieval system based on deep learning. In: International Symposium on Parallel Architecture, Algorithm and Programming, vol 729, pp. 376\u2013385. Springer, Singapore","DOI":"10.1007\/978-981-10-6442-5_34"},{"key":"6081_CR22","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1007\/s00371-019-01763-x","volume":"36","author":"Z Cao","year":"2020","unstructured":"Cao Z, Shaomin Mu, Dong M (2020) Two-attribute e-commerce image classification based on a convolutional neural network. Vis Comput 36:1619\u20131634","journal-title":"Vis Comput"},{"key":"6081_CR23","doi-asserted-by":"crossref","unstructured":"Huang G et al. (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"key":"6081_CR24","unstructured":"Howard AG et al. (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv:1704.04861. pp 1\u20139"},{"key":"6081_CR25","unstructured":"Sifre L, Mallat S (2014) Rigid-motion scattering for image classification. Ph. D. thesis"},{"key":"6081_CR26","unstructured":"Kaiser L, Gomez AN, Chollet F (2017) Depthwise separable convolutions for neural machine translation, arXiv preprint arXiv:1706.03059. pp 1\u201310."},{"key":"6081_CR27","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"6081_CR28","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp 315\u2013323"},{"key":"6081_CR29","doi-asserted-by":"crossref","unstructured":"Zoph B et al. (2018) Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8697\u20138710","DOI":"10.1109\/CVPR.2018.00907"},{"key":"6081_CR30","doi-asserted-by":"crossref","unstructured":"Zhang X et al. (2018) Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6848\u20136856","DOI":"10.1109\/CVPR.2018.00716"},{"key":"6081_CR31","unstructured":"Tan M, Le QV (2019) Efficientnet: rethinking model scaling for convolutional neural networks, arXiv preprint arXiv:1905.11946, pp 6105\u20136114"},{"key":"6081_CR32","doi-asserted-by":"crossref","unstructured":"Lin T-Y et al. (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"6081_CR33","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization, arXiv preprint arXiv:1412.6980,  pp 1\u201315"},{"key":"6081_CR34","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.neucom.2017.01.018","volume":"235","author":"MM Ghazi","year":"2017","unstructured":"Ghazi MM, Yanikoglu B, Aptoula E (2017) Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 235:228\u2013235","journal-title":"Neurocomputing"},{"key":"6081_CR35","doi-asserted-by":"crossref","unstructured":"Sandler M et al. (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"6081_CR36","unstructured":"Wang RJ, Li X, Ling CX (2018) Pelee: a real-time object detection system on mobile devices. Advances in Neural Information Processing Systems, pp 1\u201310"},{"issue":"10","key":"6081_CR37","doi-asserted-by":"publisher","first-page":"2636","DOI":"10.1080\/03610918.2014.931971","volume":"44","author":"DG Pereira","year":"2015","unstructured":"Pereira DG, Afonso A, Medeiros FM (2015) Overview of Friedman\u2019s test and post-hoc analysis. Commun Statis Simul Comput 44(10):2636\u20132653","journal-title":"Commun Statis Simul Comput"},{"key":"6081_CR38","doi-asserted-by":"crossref","unstructured":"Irigaray D et al. (2019) Accelerating the calculation of Friedman test tables on many-core processors. In: Latin American High Performance Computing Conference. Springer, Cham, pp 122\u2013135","DOI":"10.1007\/978-3-030-41005-6_9"},{"key":"6081_CR39","doi-asserted-by":"crossref","unstructured":"Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), vol 2. IEEE, pp 2169\u20132178","DOI":"10.1109\/CVPR.2006.68"},{"key":"6081_CR40","unstructured":"Li F-F, Fergus R, Perona P (2004) Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In: 2004 conference on computer vision and pattern recognition workshop. IEEE, pp 178\u2013178"},{"key":"6081_CR41","doi-asserted-by":"crossref","unstructured":"Chua T-S et al. (2009) NUS-WIDE: a real-world web image database from National University of Singapore. In: Proceedings of the ACM international conference on image and video retrieval, pp 1\u20139","DOI":"10.1145\/1646396.1646452"},{"key":"6081_CR42","doi-asserted-by":"crossref","unstructured":"Yang J et al. (2009) Linear spatial pyramid matching using sparse coding for image classification. In: 2009 IEEE Conference on computer vision and pattern recognition. IEEE, pp 1794\u20131801","DOI":"10.1109\/CVPR.2009.5206757"},{"key":"6081_CR43","doi-asserted-by":"crossref","unstructured":"Rasiwasia N, Vasconcelos N (2008) Scene classification with low-dimensional semantic spaces and weak supervision. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp 1\u20136","DOI":"10.1109\/CVPR.2008.4587372"},{"key":"6081_CR44","first-page":"1378","volume":"23","author":"L Li","year":"2010","unstructured":"Li L et al (2010) Object bank: a high-level image representation for scene classification & semantic feature sparsification. Adv Neural Inf Process Syst 23:1378\u20131386","journal-title":"Adv Neural Inf Process Syst"},{"issue":"2019","key":"6081_CR45","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.jvcir.2019.05.016","volume":"62","author":"Y Sun","year":"2019","unstructured":"Sun Y et al (2019) Image classification base on PCA of multi-view deep representation. J Vis Commun Image Repres 62(2019):253\u2013258","journal-title":"J Vis Commun Image Repres"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06081-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06081-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06081-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T06:05:54Z","timestamp":1635055554000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06081-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,6]]},"references-count":45,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["6081"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06081-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,6]]},"assertion":[{"value":"24 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}