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Syst."],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The paper summarizes the research progress on critical region recognition and deep metric learning to achieve accurate clothing image retrieval in cross-domain situations. Critical region recognition is of great value for the clothing feature extraction, effectively improving retrieval accuracy. The accuracy will decrease when solving difficult samples with similar features but different categories. Nowadays, deep metric learning is an effective way to solve this problem, which utilizes the optimization of different loss functions and ensemble network to strengthen the discrimination of clothing features. Therefore, through comparison of the experimental results of different algorithms and analysis of the accuracy of cross-domain clothing retrieval, it is demonstrated that the improvement of the retrieval accuracy in the future mainly depends on clothing important feature extraction and clothing feature discrimination.<\/jats:p>","DOI":"10.1007\/s40747-022-00750-5","type":"journal-article","created":{"date-parts":[[2022,5,13]],"date-time":"2022-05-13T06:02:57Z","timestamp":1652421777000},"page":"5531-5544","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Survey on clothing image retrieval with cross-domain"],"prefix":"10.1007","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0056-0337","authenticated-orcid":false,"given":"Chen","family":"Ning","sequence":"first","affiliation":[]},{"given":"Yang","family":"Di","sequence":"additional","affiliation":[]},{"given":"Li","family":"Menglu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,13]]},"reference":[{"key":"750_CR1","unstructured":"Korea Federation of Textile Industries (2019) Korea fashion market trend 2019 Report; Korea Federation of Textile Industries: Seoul, Korea"},{"key":"750_CR2","unstructured":"Korea Fashion Association (2019) Global fashion industry survey. 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