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However, the current mainstream retrieval methods are still limited to using text or exemplar images as input. For huge commodity databases, it remains a long\u2010standing unsolved problem for users to find the interested products quickly. Different from the traditional text\u2010based and exemplar\u2010based image retrieval techniques, sketch\u2010based image retrieval (SBIR) provides a more intuitive and natural way for users to specify their search need. Due to the large cross\u2010domain discrepancy between the free\u2010hand sketch and fashion images, retrieving fashion images by sketches is a significantly challenging task. In this work, we propose a new algorithm for sketch\u2010based fashion image retrieval based on cross\u2010domain transformation. In our approach, the sketch and photo are first transformed into the same domain. Then, the sketch domain similarity and the photo domain similarity are calculated, respectively, and fused to improve the retrieval accuracy of fashion images. Moreover, the existing fashion image datasets mostly contain photos only and rarely contain the sketch\u2010photo pairs. Thus, we contribute a fine\u2010grained sketch\u2010based fashion image retrieval dataset, which includes 36,074 sketch\u2010photo pairs. Specifically, when retrieving on our Fashion Image dataset, the accuracy of our model ranks the correct match at the top\u20101 which is 96.6%, 92.1%, 91.0%, and 90.5% for clothes, pants, skirts, and shoes, respectively. Extensive experiments conducted on our dataset and two fine\u2010grained instance\u2010level datasets, i.e., QMUL\u2010shoes and QMUL\u2010chairs, show that our model has achieved a better performance than other existing methods.<\/jats:p>","DOI":"10.1155\/2021\/5577735","type":"journal-article","created":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T18:20:09Z","timestamp":1621966809000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A New Algorithm for Sketch\u2010Based Fashion Image Retrieval Based on Cross\u2010Domain Transformation"],"prefix":"10.1155","volume":"2021","author":[{"given":"Haopeng","family":"Lei","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8153-7392","authenticated-orcid":false,"given":"Simin","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Mingwen","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiangjian","family":"He","sequence":"additional","affiliation":[]},{"given":"Wenjing","family":"Jia","sequence":"additional","affiliation":[]},{"given":"Sibo","family":"Li","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,5,25]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"crossref","unstructured":"KalantidisY. 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