{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T12:29:06Z","timestamp":1750681746502,"version":"3.37.3"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"25","license":[{"start":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T00:00:00Z","timestamp":1715904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T00:00:00Z","timestamp":1715904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Luxembourg Ministry of Economy","award":["119N537"],"award-info":[{"award-number":["119N537"]}]},{"DOI":"10.13039\/501100004410","name":"T\u00dcrkiye Bilimsel ve Teknolojik Arastirma Kurumu","doi-asserted-by":"publisher","award":["119N537"],"award-info":[{"award-number":["119N537"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100024102","name":"SINTEF","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100024102","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The fashion industry\u2019s traditional price-setting methods, based on historical sales and Fashion Week trends, are inadequate in the digital era. Rapid changes in collections and consumer preferences necessitate advanced Artificial Intelligence (AI) techniques. These AI methods should analyze data from various sources, including social media and e-commerce, to predict future fashion trends and prices. In this paper, we propose, apply, and assess a data analytics approach, i.e., FashionXpert, employing several image processing and machine learning techniques in an AI pipeline for garment price prediction. It integrates various heterogeneous data sources (e.g., textual and image data from e-stores, brand websites, and social media) to obtain more consistent, accurate, and beneficial information. We evaluated its effectiveness with an industrial data set obtained by a fashion search tool from the electronic commerce sites of clothing brands. FashionXpert predicted garment prices with an average Mean Absolute Error (MAE) of 15.31 EUR on a data set that has a standard deviation of 72.99 EUR.<\/jats:p>","DOI":"10.1007\/s00521-024-09901-w","type":"journal-article","created":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T12:02:08Z","timestamp":1715947328000},"page":"15631-15651","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["An AI pipeline for garment price projection using computer vision"],"prefix":"10.1007","volume":"36","author":[{"given":"Rodrigo","family":"Rico G\u00f3mez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joe","family":"Lorentz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Hartmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2170-2066","authenticated-orcid":false,"given":"Arda","family":"Goknil","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Inder","family":"Pal Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tayfun G\u00f6kmen","family":"Hala\u00e7","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G\u00fclnaz","family":"Boruzanl\u0131 Ekinci","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,17]]},"reference":[{"key":"9901_CR1","unstructured":"follow.fashion. https:\/\/follow.fashion\/"},{"key":"9901_CR2","unstructured":"Edited. https:\/\/edited.com\/"},{"key":"9901_CR3","doi-asserted-by":"crossref","unstructured":"Liu Z, Luo P, Qiu S, Wang X, Tang X (2016) Deepfashion: powering robust clothes recognition and retrieval with rich annotations. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1096\u20131104","DOI":"10.1109\/CVPR.2016.124"},{"key":"9901_CR4","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask-RCNN. arXiv preprint arXiv:1703.06870v3","DOI":"10.1109\/ICCV.2017.322"},{"key":"9901_CR5","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"9901_CR6","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. arXiv preprint arXiv:1505.04597v1","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"9901_CR7","doi-asserted-by":"crossref","unstructured":"hen K, Chen K, Cong P, Hsu WH, Luo J (2015) Who are the devils wearing prada in new york city? In: Proceedings of the 23rd ACM international conference on multimedia","DOI":"10.1145\/2733373.2809930"},{"key":"9901_CR8","doi-asserted-by":"publisher","unstructured":"Yamaguchi K, Kiapour MH, Ortiz LE, Berg TL (2012) Parsing clothing in fashion photographs. In: 2012 IEEE conference on computer vision and pattern recognition, pp 3570\u20133577. https:\/\/doi.org\/10.1109\/CVPR.2012.6248101","DOI":"10.1109\/CVPR.2012.6248101"},{"issue":"4","key":"9901_CR9","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1145\/3447239","volume":"54","author":"WH Cheng","year":"2021","unstructured":"Cheng WH, Song S, Chen CY, Hidayati SC (2021) Fashion meets computer vision: a survey. ACM Comput Surv 54(4):72. https:\/\/doi.org\/10.1145\/3447239","journal-title":"ACM Comput Surv"},{"key":"9901_CR10","doi-asserted-by":"publisher","unstructured":"Borr\u00e0s A, Tous F, Llad\u00f3s J, Vanrell M (2003) High-level clothes description based on colour-texture and structural features. In: Perales FJ, Campilho AJC, de la Blanca NP, Sanfeliu A (eds) Pattern recognition and image analysis. IbPRIA 2003. Lecture notes in computer science, vol 2652. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/978-3-540-44871-6_13","DOI":"10.1007\/978-3-540-44871-6_13"},{"issue":"12","key":"9901_CR11","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1109\/TPAMI.2015.2408360","volume":"37","author":"X Liang","year":"2015","unstructured":"Liang X et al (2015) Deep human parsing with active template regression. IEEE Trans Pattern Anal Mach Intell 37(12):2402\u20132414. https:\/\/doi.org\/10.1109\/TPAMI.2015.2408360","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9901_CR12","doi-asserted-by":"publisher","unstructured":"Kiapour MH, Han X, Lazebnik S, Berg AC, Berg TL (2015) Where to buy it: matching street clothing photos in online shops. In: 2015 IEEE international conference on computer vision (ICCV), pp 3343\u20133351. https:\/\/doi.org\/10.1109\/ICCV.2015.382","DOI":"10.1109\/ICCV.2015.382"},{"key":"9901_CR13","doi-asserted-by":"crossref","unstructured":"Jin B, Lu B, Wu H, Shi W, Li Y (2021) Fashion style forecasts based on different price ranges. In: 2021 IEEE 5th advanced information technology, electronic and automation control conference (IAEAC), vol 5. IEEE, pp 2296\u20132302","DOI":"10.1109\/IAEAC50856.2021.9390629"},{"key":"9901_CR14","doi-asserted-by":"crossref","unstructured":"Al-Halah Z, Stiefelhagen R, Grauman K (2017) Fashion forward: forecasting visual style in fashion. In: Proceedings of the IEEE international conference on computer vision, pp 388\u2013397","DOI":"10.1109\/ICCV.2017.50"},{"key":"9901_CR15","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, pp 1097\u20131105"},{"key":"9901_CR16","unstructured":"Jia M et\u00a0al (2020) Fashionpedia: ontology, segmentation, and an attribute localization dataset. In: Vedaldi A, Bischof H, Brox T, Frahm JM (eds) Computer vision\u2014ECCV 2020. ECCV 2020. Lecture notes in computer science, vol 12346. Springer, Cham"},{"key":"9901_CR17","doi-asserted-by":"publisher","unstructured":"Sreekumar A, Geetha M (2020) Hand segmentation in complex background using UNet. In: 2020 2nd international conference on inventive research in computing applications (ICIRCA), pp 440\u2013445. https:\/\/doi.org\/10.1109\/ICIRCA48905.2020.9183215","DOI":"10.1109\/ICIRCA48905.2020.9183215"},{"key":"9901_CR18","doi-asserted-by":"publisher","unstructured":"Xie H -X, Lin C -Y, Zheng H, Lin P -Y (2018) An UNet-based head shoulder segmentation network. In: 2018 IEEE international conference on consumer electronics-Taiwan (ICCE-TW), pp 1\u20132. https:\/\/doi.org\/10.1109\/ICCE-China.2018.8448587","DOI":"10.1109\/ICCE-China.2018.8448587"},{"key":"9901_CR19","doi-asserted-by":"crossref","unstructured":"Pavan Kumar I, Hara Gopal VP, Ramasubbareddy S, Nalluri S, Govinda K (2020) Dominant color palette extraction by K-means clustering algorithm and reconstruction of image. In: Raju K, Senkerik R, Lanka S, Rajagopal V (eds) Data engineering and communication technology. Advances in intelligent systems and computing, vol 1079. Springer, Singapore","DOI":"10.1007\/978-981-15-1097-7_78"},{"key":"9901_CR20","doi-asserted-by":"publisher","unstructured":"Jin X, Han J (2011) K-means clustering. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Boston. https:\/\/doi.org\/10.1007\/978-0-387-30164-8-425","DOI":"10.1007\/978-0-387-30164-8-425"},{"key":"9901_CR21","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J (2021) An image is worth $$16\\times 16$$ words: transformers for image recognition at scale. Computer Science, Computer Vision and Pattern Recognition.arXiv:2010.11929"},{"key":"9901_CR22","doi-asserted-by":"crossref","unstructured":"He K, Chen X, Xie S, Li Y, Doll\u00e1r P, Girshick R (2021) Masked autoencoders are scalable vision learners. Computer Science, Computer Vision and Pattern Recognition, arXiv","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"9901_CR23","doi-asserted-by":"crossref","unstructured":"Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, Xiao T, Whitehead S, Berg AC, Lo WY, Doll\u00e1r P (2023) Segment anything. Computer Science, Computer Vision and Pattern Recognition. arXiv:2304.02643","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"9901_CR24","unstructured":"Li Z, Nie Y, Han K, Guo J, Xie L, Wang Y (2022) A transformer-based object detector with coarse-fine crossing representations. In: 2022, 36th conference on neural information processing systems (NeurIPS 2022)"},{"key":"9901_CR25","doi-asserted-by":"crossref","unstructured":"He L, Todorovic S (2022) DESTR: object detection With split transformer. In: 2022, proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 9377\u20139386","DOI":"10.1109\/CVPR52688.2022.00916"},{"key":"9901_CR26","unstructured":"Kedia S, Jain S, Sharma A (2020) Price optimization in fashion e-commerce. arXiv preprint arxiv:2007.05216"},{"key":"9901_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2020.102282","volume":"57","author":"HD Nguyen","year":"2021","unstructured":"Nguyen HD, Tran KP, Thomassey S, Hamad M (2021) Forecasting and anomaly detection approaches using LSTM and LSTM autoencoder techniques with the applications in supply chain management. Int J Inf Manag 57:102282. https:\/\/doi.org\/10.1016\/j.ijinfomgt.2020.102282","journal-title":"Int J Inf Manag"},{"key":"9901_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.elerap.2022.101118","volume":"51","author":"QQ He","year":"2022","unstructured":"He QQ, Wu C, Si YW (2022) LSTM with particle swam optimization for sales forecasting. Electron Commer Res Appl 51:101118. https:\/\/doi.org\/10.1016\/j.elerap.2022.101118","journal-title":"Electron Commer Res Appl"},{"issue":"8","key":"9901_CR29","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"9901_CR30","doi-asserted-by":"publisher","first-page":"30898","DOI":"10.1109\/ACCESS.2021.3058133","volume":"9","author":"GDC Ferreira Fernando","year":"2021","unstructured":"Ferreira Fernando GDC, Gandomi Amir H, Cardoso Rodrigo TN (2021) Artificial intelligence applied to stock market trading: a review. IEEE Access 9:30898\u201330917","journal-title":"IEEE Access"},{"key":"9901_CR31","doi-asserted-by":"crossref","unstructured":"Warner B, Crook A, Cao R (2020) Predicting the DJIA with news headlines and historic data using hybrid genetic algorithm\/support vector regression and BERT. In: Big data\u2013BigData 2020: 9th international conference, held as part of the services conference federation, SCF 2020, Honolulu, HI, USA, September 18\u201320, 2020, Proceedings 9. Springer, pp 23\u201337","DOI":"10.1007\/978-3-030-59612-5_3"},{"key":"9901_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106898","volume":"99","author":"DK Mohanty","year":"2021","unstructured":"Mohanty DK, Parida AK, Khuntia SS (2021) Financial market prediction under deep learning framework using auto encoder and kernel extreme learning machine. Appl Soft Comput 99:106898","journal-title":"Appl Soft Comput"},{"key":"9901_CR33","doi-asserted-by":"crossref","unstructured":"Mokhtari S, Yen KK, Liu J (2021) Effectiveness of artificial intelligence in stock market prediction based on machine learning. arXiv preprint arXiv:2107.01031","DOI":"10.5120\/ijca2021921347"},{"issue":"5","key":"9901_CR34","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1080\/00405000.2023.2201978","volume":"115","author":"FG Ya\u015far \u00c7\u0131kla\u00e7and\u0131r","year":"2023","unstructured":"Ya\u015far \u00c7\u0131kla\u00e7and\u0131r FG, Utku S, \u00d6zdemir H (2023) Determination of various fabric defects using different machine learning techniques. J Text  Inst 115(5):733\u2013743","journal-title":"J Text Inst"},{"key":"9901_CR35","unstructured":"Zhong S, Ribul M, Cho Y, Obrist M (2023) TextileNet: a material taxonomy-based fashion textile dataset. arXiv preprint arXiv:2301.06160"},{"issue":"1","key":"9901_CR36","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1080\/15440478.2020.1727817","volume":"19","author":"W Xing","year":"2022","unstructured":"Xing W, Liu Y, Xin B, Zang L, Deng N (2022) The application of deep and transfer learning for identifying cashmere and wool fibers. J Nat Fibers 19(1):88\u2013104","journal-title":"J Nat Fibers"},{"issue":"1","key":"9901_CR37","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1186\/s40537-023-00830-4","volume":"10","author":"Y Zhu","year":"2023","unstructured":"Zhu Y, Liu R, Hu G, Chen X, Li W (2023) Accurate identification of cashmere and wool fibers based on enhanced ShuffleNetV2 and transfer learning. J Big Data 10(1):152","journal-title":"J Big Data"},{"issue":"5\u20136","key":"9901_CR38","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1177\/00405175221130773","volume":"93","author":"Y Kahraman","year":"2023","unstructured":"Kahraman Y, Durmu\u015fo\u011flu A (2023) Deep learning-based fabric defect detection: a review. Text Res J 93(5\u20136):1485\u20131503","journal-title":"Text Res J"},{"key":"9901_CR39","unstructured":"Plot entropy in Scikimage post. https:\/\/scikit-image.org\/docs\/dev\/auto-examples\/filters\/plot-entropy.html"},{"key":"9901_CR40","doi-asserted-by":"publisher","unstructured":"Zou X, Kong X, Wong W, Wang C, Liu Y, Cao Y (2019) FashionAI: a hierarchical dataset for fashion understanding. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition workshops (CVPRW), pp 296\u2013304. https:\/\/doi.org\/10.1109\/CVPRW.2019.00039","DOI":"10.1109\/CVPRW.2019.00039"},{"key":"9901_CR41","doi-asserted-by":"crossref","unstructured":"Ge Y, Zhang R, Wang X, Tang X, Luo P (2019) DeepFashion2: a versatile benchmark for detection, pose estimation. In: Segmentation and re-identification of clothing images, CVPR","DOI":"10.1109\/CVPR.2019.00548"},{"key":"9901_CR42","doi-asserted-by":"crossref","unstructured":"Guo S, Huang W, Zhang X, Srikhanta P, Cui Y, Li Y, Adam H, Scott MR, Belongie S (2019) The iMaterialist fashion attribute dataset. In: Proceedings of the IEEE\/CVF international conference on computer vision workshops","DOI":"10.1109\/ICCVW.2019.00377"},{"issue":"3\/4","key":"9901_CR43","doi-asserted-by":"publisher","first-page":"591","DOI":"10.2307\/2333709","volume":"52","author":"SS Shapiro","year":"1965","unstructured":"Shapiro SS, Wilk MB (1965) An analysis of variance test for normality (complete samples). Biometrika 52(3\/4):591\u2013611. https:\/\/doi.org\/10.2307\/2333709","journal-title":"Biometrika"},{"key":"9901_CR44","doi-asserted-by":"publisher","unstructured":"Nachar N (2008) The Mann\u2013Whitney U: a test for assessing whether two independent samples come from the same distribution. Tutor Quant Methods Psychol. https:\/\/doi.org\/10.20982\/tqmp.04.1.p013","DOI":"10.20982\/tqmp.04.1.p013"},{"key":"9901_CR45","doi-asserted-by":"crossref","unstructured":"Lin TY, et al (2014) Microsoft coco: common objects in context. In: Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6\u201312, 2014, Proceedings, Part V 13. Springer International Publishing, pp 740\u2013755","DOI":"10.1007\/978-3-319-10602-1_48"},{"issue":"10","key":"9901_CR46","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09901-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-09901-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-09901-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T14:28:29Z","timestamp":1724423309000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-09901-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,17]]},"references-count":46,"journal-issue":{"issue":"25","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["9901"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-09901-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2024,5,17]]},"assertion":[{"value":"16 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2024","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 that there is no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}