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This requires new AI-empowered technologies that enable detection, classification, pairing, and quality assessment in a viable automatic process. This article discusses automatic shoe pairing, which comprises two sequential stages: a) deep multiview shoe embedding (compact representation of multiview data); and b) clustering of shoes\u2019 embeddings with a fixed similarity threshold to return sets of possible pairs. Each shoe in our pipeline is represented by multiple images that are collected in industrial darkrooms. We present various approaches to shoe pairing\u2014from fully unsupervised ones based on image descriptors to supervised ones that rely on deep neural networks\u2014to identify the most effective one for this highly specific industrial task. The article also explains how the selected method can be improved by hyperparameter tuning, massive increases in training data, and data augmentation.<\/jats:p>","DOI":"10.1007\/978-3-031-37649-8_4","type":"book-chapter","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T04:02:08Z","timestamp":1690257728000},"page":"35-44","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Novel Process of\u00a0Shoe Pairing Using Computer Vision and\u00a0Deep Learning Methods"],"prefix":"10.1007","author":[{"given":"Marek","family":"Koz\u0142owski","sequence":"first","affiliation":[]},{"given":"Przemyslaw","family":"Buczkowski","sequence":"additional","affiliation":[]},{"given":"Piotr","family":"Brzezinski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"issue":"4","key":"4_CR1","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1109\/TIP.2011.2173206","volume":"21","author":"D Brunet","year":"2011","unstructured":"Brunet, D., Vrscay, E.R., Wang, Z.: On the mathematical properties of the structural similarity index. 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