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Accordingly, there is an increasing demand for a new efficient method to retrieve clothing images by using mobile visual sensors. Different from traditional keyword-based and content-based image retrieval techniques, sketch-based image retrieval provides a more intuitive and natural way for users to clarify their search need. However, this is a challenging problem due to the large discrepancy between sketches and images. To tackle this problem, we present a new sketch-based clothing image retrieval algorithm based on sketch component segmentation. The proposed strategy is to first collect a large scale of clothing sketches and images and tag with semantic component labels for training dataset, and then, we employ conditional random field model to train a classifier which is used to segment query sketch into different components. After that, several feature descriptors are fused to describe each component and capture the topological information. Finally, a dynamic component-weighting strategy is established to boost the effect of important components when measuring similarities. The approach is evaluated on a large, real-world clothing image dataset, and experimental results demonstrate the effectiveness and good performance of the proposed method. <\/jats:p>","DOI":"10.1177\/1550147718815627","type":"journal-article","created":{"date-parts":[[2018,11,30]],"date-time":"2018-11-30T07:00:05Z","timestamp":1543561205000},"page":"155014771881562","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["A new clothing image retrieval algorithm based on sketch component segmentation in mobile visual sensors"],"prefix":"10.1177","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1988-4155","authenticated-orcid":false,"given":"Haopeng","family":"Lei","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 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