Part-based clothing image annotation by visual neighbor retrieval

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• 作者：Guang-Lu Sun ^{sunguanglu66@126.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Xiao Wu ; ^{wuxiaohk@home.swjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Qiang Peng ^{qpeng@home.swjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Image annotation ; Clothing search ; Part-based ; Annotation by search
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：12 November 2016
• 年：2016
• 卷：213
• 期：Complete
• 页码：115-124
• 全文大小：3643 K

文摘

With the advent and popularity of e-commerce and clothing image-sharing websites, clothing image search and annotation become active research topics in recent years. Clothing image annotation is a challenging task due to large variations in clothing appearance, human body pose and background. In this paper, we explore part-based clothing image annotation in a search and mining framework. Similar image search is first conducted to discover visual neighbors for a query image. The impact of large variations of clothing is alleviated by pose detection and part-based feature alignment. Tag relevance and tag saliency are taken into consideration to obtain the candidate tags. The relevance of candidate tags is identified by mining visual neighbors of a query image, while the saliency is determined according to the relationship between query image parts and part clusters on the whole training set. Experiments on a dataset with 1.1 million clothing images demonstrate the effectiveness and efficiency of the proposed approach.