{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T18:20:35Z","timestamp":1732040435537},"reference-count":45,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,4,17]],"date-time":"2021-04-17T00:00:00Z","timestamp":1618617600000},"content-version":"vor","delay-in-days":106,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1504608","62072414"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2021,1]]},"abstract":"In order to improve recognition accuracy of clothing style and fully exploit the advantages of deep learning in extracting deep semantic features from global to local features of clothing images, this paper utilizes the target detection technology and deep residual network (ResNet) to extract comprehensive clothing features, which aims at focusing on clothing itself in the process of feature extraction procedure. Based on that, we propose a multideep feature fusion algorithm for clothing image style recognition. First, we use the improved target detection model to extract the global area, main part, and part areas of clothing, which constitute the image, so as to weaken the influence of the background and other interference factors. Then, the three parts were inputted, respectively, to improve ResNet for feature extraction, which has been trained beforehand. The ResNet model is improved by optimizing the convolution layer in the residual block and adjusting the order of the batch\u2010normalized layer and the activation layer. Finally, the multicategory fusion features were obtained by combining the overall features of the clothing image from the global area, the main part, to the part areas. The experimental results show that the proposed algorithm eliminates the influence of interference factors, makes the recognition process focus on clothing itself, greatly improves the accuracy of the clothing style recognition, and is better than the traditional deep residual network\u2010based methods.<\/jats:p>","DOI":"10.1155\/2021\/5577393","type":"journal-article","created":{"date-parts":[[2021,4,17]],"date-time":"2021-04-17T20:35:11Z","timestamp":1618691711000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multideep Feature Fusion Algorithm for Clothing Style Recognition"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-1866-6607","authenticated-orcid":false,"given":"Yuhua","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4019-4669","authenticated-orcid":false,"given":"Zhiqiang","family":"He","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-1836-1360","authenticated-orcid":false,"given":"Sunan","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4286-5120","authenticated-orcid":false,"given":"Zicheng","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7266-8830","authenticated-orcid":false,"given":"Wanwei","family":"Huang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,4,17]]},"reference":[{"key":"e_1_2_9_1_2","first-page":"255","article-title":"Clothing image classification and retrieval based on metric learning","volume":"34","author":"Wang Y.","year":"2017","journal-title":"Computer Applications and Software"},{"key":"e_1_2_9_2_2","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan K.","year":"2015","journal-title":"International Conference on Learning Representations"},{"doi-asserted-by":"publisher","key":"e_1_2_9_3_2","DOI":"10.1016\/j.imavis.2020.104090"},{"doi-asserted-by":"publisher","key":"e_1_2_9_4_2","DOI":"10.1111\/exsy.12541"},{"doi-asserted-by":"publisher","key":"e_1_2_9_5_2","DOI":"10.1007\/s11042-020-09408-1"},{"doi-asserted-by":"publisher","key":"e_1_2_9_6_2","DOI":"10.32604\/cmes.2020.011380"},{"doi-asserted-by":"publisher","key":"e_1_2_9_7_2","DOI":"10.3390\/su12125037"},{"doi-asserted-by":"crossref","unstructured":"HeZ. LiY. DengL. LiP. ShiX. andHanX. A new two-stage image retrieval algorithm with convolutional neural network Proceedings of the 2019 8th International Conference on Networks Communication and Computing 2019 Luoyang Henan China 98\u2013102 https:\/\/doi.org\/10.1145\/3375998.3376014.","key":"e_1_2_9_8_2","DOI":"10.1145\/3375998.3376014"},{"doi-asserted-by":"publisher","key":"e_1_2_9_9_2","DOI":"10.1109\/72.279181"},{"doi-asserted-by":"crossref","unstructured":"SzegedyC. VanhouckeV. IoffeS. ShlensJ. andWojnaZ. Rethinking the inception architecture for computer vision 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 Las Vegas NV USA 2818\u20132826 https:\/\/doi.org\/10.1109\/CVPR.2016.308 2-s2.0-84986296808.","key":"e_1_2_9_10_2","DOI":"10.1109\/CVPR.2016.308"},{"doi-asserted-by":"publisher","key":"e_1_2_9_11_2","DOI":"10.1016\/j.neunet.2017.07.002"},{"doi-asserted-by":"publisher","key":"e_1_2_9_12_2","DOI":"10.1023\/b:visi.0000029664.99615.94"},{"doi-asserted-by":"crossref","unstructured":"SchroffF. KalenichenkoD. andPhilbinJ. FaceNet: a unified embedding for face recognition and clustering 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 Boston MA USA 815\u2013823 https:\/\/doi.org\/10.1109\/CVPR.2015.7298682 2-s2.0-84946751287.","key":"e_1_2_9_13_2","DOI":"10.1109\/CVPR.2015.7298682"},{"doi-asserted-by":"crossref","unstructured":"ZhengY. WuS. LiuD. WeiR. LiS. andTuZ. Sleeper defect detection based on improved YOLO V3 algorithm 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA) 2020 Kristiansand Norway 955\u2013960 https:\/\/doi.org\/10.1109\/ICIEA48937.2020.9248299.","key":"e_1_2_9_14_2","DOI":"10.1109\/ICIEA48937.2020.9248299"},{"doi-asserted-by":"crossref","unstructured":"YangX.andLateckiL. J. Affinity learning on a tensor product graph with applications to shape and image retrieval Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2011 2011 Colorado Springs CO USA 2369\u20132376 https:\/\/doi.org\/10.1109\/CVPR.2011.5995325 2-s2.0-80052899384.","key":"e_1_2_9_15_2","DOI":"10.1109\/CVPR.2011.5995325"},{"doi-asserted-by":"crossref","unstructured":"NohH. AraujoA. andSimJ. Large-scale image retrieval with attentive deep local features 2017 IEEE International Conference on Computer Vision (ICCV) 2017 Venice Italy 3476\u20133485 https:\/\/doi.org\/10.1109\/ICCV.2017.374 2-s2.0-85041915670.","key":"e_1_2_9_16_2","DOI":"10.1109\/ICCV.2017.374"},{"doi-asserted-by":"publisher","key":"e_1_2_9_17_2","DOI":"10.1109\/TPAMI.2009.77"},{"doi-asserted-by":"publisher","key":"e_1_2_9_18_2","DOI":"10.3724\/SP.J.1016.2011.02224"},{"doi-asserted-by":"publisher","key":"e_1_2_9_19_2","DOI":"10.1007\/978-1-4842-2766-4_5"},{"unstructured":"RedmonJ.andFarhadiA. YOLOv3: an incremental improvement 2018 http:\/\/arxiv.org\/abs\/1804.02767.","key":"e_1_2_9_20_2"},{"doi-asserted-by":"publisher","key":"e_1_2_9_21_2","DOI":"10.1007\/s11042-020-08928-0"},{"doi-asserted-by":"publisher","key":"e_1_2_9_22_2","DOI":"10.1007\/s11042-020-08852-3"},{"doi-asserted-by":"publisher","key":"e_1_2_9_23_2","DOI":"10.1007\/s11042-020-08806-9"},{"doi-asserted-by":"crossref","unstructured":"YamaguchiK. KiapourM. H. andBergT. L. Paper doll parsing: retrieving similar styles to parse clothing items 2013 IEEE International Conference on Computer Vision 2013 Sydney NSW Australia 3519\u20133526 https:\/\/doi.org\/10.1109\/ICCV.2013.437 2-s2.0-84898779488.","key":"e_1_2_9_24_2","DOI":"10.1109\/ICCV.2013.437"},{"doi-asserted-by":"crossref","unstructured":"SzegedyC. IoffeS. andVanhouckeV. Inception \u2043 v4 inceptionresnet and the impact of residual connections on learning 2016 http:\/\/arxiv.org\/abs\/1602.07261.","key":"e_1_2_9_25_2","DOI":"10.1609\/aaai.v31i1.11231"},{"doi-asserted-by":"publisher","key":"e_1_2_9_26_2","DOI":"10.1109\/tmm.2013.2279658"},{"doi-asserted-by":"publisher","key":"e_1_2_9_27_2","DOI":"10.1177\/0165551518782825"},{"doi-asserted-by":"crossref","unstructured":"KimH. J. DunnE. andFrahmJ.-M. Predicting good features for image geo-localization using per-bundle VLAD 2015 IEEE International Conference on Computer Vision (ICCV) 2015 Santiago Chile 1170\u20131178 https:\/\/doi.org\/10.1109\/ICCV.2015.139 2-s2.0-84973879896.","key":"e_1_2_9_28_2","DOI":"10.1109\/ICCV.2015.139"},{"doi-asserted-by":"publisher","key":"e_1_2_9_29_2","DOI":"10.1007\/s11042-017-4446-y"},{"doi-asserted-by":"crossref","unstructured":"AzizpourH. RazavianA. S. SullivanJ. MakiA. andCarlssonS. From generic to specific deep representations for visual recognition 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2015 Boston MA USA 36\u201345 https:\/\/doi.org\/10.1109\/CVPRW.2015.7301270 2-s2.0-84951960494.","key":"e_1_2_9_30_2","DOI":"10.1109\/CVPRW.2015.7301270"},{"doi-asserted-by":"crossref","unstructured":"HanX. LiY. ZhengQ.et al. A Multiple Feature Fusion Based Image Retrieval Algorithm Proceedings of 2019 the 8th International Conference on Networks Communication and Computing 2019 Luoyang Henan China 104\u2013109 https:\/\/doi.org\/10.1145\/3375998.3376014.","key":"e_1_2_9_31_2","DOI":"10.1145\/3375998.3376015"},{"doi-asserted-by":"crossref","unstructured":"WeiL. ZhangS. andYaoH. GLAD: global-local-alignment descriptor for pedestrian retrieval Proceedings of the 25th ACM international conference on Multimedia 2017 Buenos Aires Argentina 420\u2013428.","key":"e_1_2_9_32_2","DOI":"10.1145\/3123266.3123279"},{"doi-asserted-by":"crossref","unstructured":"MishkinD. RadenovicF. andMatasJ. Repeatability is not enough: learning affine regions via discriminability Proceedings of the European Conference on Computer Vision (ECCV) 2018 Munich Germany 284\u2013300.","key":"e_1_2_9_33_2","DOI":"10.1007\/978-3-030-01240-3_18"},{"doi-asserted-by":"crossref","unstructured":"PhilbinJ. ChumO. IsardM. SivicJ. andZissermanA. Object retrieval with large vocabularies and fast spatial matching 2007 IEEE Conference on Computer Vision and Pattern Recognition 2007 Minneapolis MN USA 1\u20138 https:\/\/doi.org\/10.1109\/CVPR.2007.383172 2-s2.0-34948903793.","key":"e_1_2_9_34_2","DOI":"10.1109\/CVPR.2007.383172"},{"doi-asserted-by":"crossref","unstructured":"GammeterS. I know what you did last summer: object-level auto-annotation of holiday snaps 2009 IEEE 12th International Conference on Computer Vision 2009 Kyoto Japan 614\u2013621 https:\/\/doi.org\/10.1109\/ICCV.2009.5459180.","key":"e_1_2_9_35_2","DOI":"10.1109\/ICCV.2009.5459180"},{"doi-asserted-by":"publisher","key":"e_1_2_9_36_2","DOI":"10.1109\/TIP.2019.2917234"},{"doi-asserted-by":"publisher","key":"e_1_2_9_37_2","DOI":"10.1109\/TPAMI.2018.2846566"},{"doi-asserted-by":"crossref","unstructured":"RadenovicF. IscenA. ToliasG. AvrithisY. andChumO. Revisiting Oxford and Paris: large-scale image retrieval benchmarking 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2018 Salt Lake City UT USA 5706\u20135715 https:\/\/doi.org\/10.1109\/CVPR.2018.00598 2-s2.0-85061232826.","key":"e_1_2_9_38_2","DOI":"10.1109\/CVPR.2018.00598"},{"doi-asserted-by":"publisher","key":"e_1_2_9_39_2","DOI":"10.1109\/TII.2018.2884951"},{"doi-asserted-by":"crossref","unstructured":"JungI. YouK. NohH.et al. Real-time object tracking via meta-learning: Efficient model adaptation and one-shot channel pruning Proceedings of the AAAI Conference on Artificial Intelligence 2020 Venice Italy 11205\u201311212.","key":"e_1_2_9_40_2","DOI":"10.1609\/aaai.v34i07.6779"},{"doi-asserted-by":"publisher","key":"e_1_2_9_41_2","DOI":"10.1109\/TII.2019.2952565"},{"doi-asserted-by":"publisher","key":"e_1_2_9_42_2","DOI":"10.1007\/s10846-017-0735-y"},{"doi-asserted-by":"publisher","key":"e_1_2_9_43_2","DOI":"10.1109\/TPAMI.2010.133"},{"doi-asserted-by":"publisher","key":"e_1_2_9_44_2","DOI":"10.1002\/ima.22015"},{"doi-asserted-by":"publisher","key":"e_1_2_9_45_2","DOI":"10.1007\/s11263-012-0600-1"}],"container-title":["Wireless Communications and Mobile Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2021\/5577393.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2021\/5577393.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/5577393","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T11:35:26Z","timestamp":1723030526000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/5577393"}},"subtitle":[],"editor":[{"given":"Amr","family":"Tolba","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/5577393"],"URL":"https:\/\/doi.org\/10.1155\/2021\/5577393","archive":["Portico"],"relation":{},"ISSN":["1530-8669","1530-8677"],"issn-type":[{"type":"print","value":"1530-8669"},{"type":"electronic","value":"1530-8677"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-01-07","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-04-03","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-04-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}