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细粒度民族服饰图像检索的全局-局部特征提取方法
引用本文:周前前,刘骊,刘利军,付晓东,黄青松. 细粒度民族服饰图像检索的全局-局部特征提取方法[J]. 模式识别与人工智能, 2021, 34(5): 463-472. DOI: 10.16451/j.cnki.issn1003-6059.202105009
作者姓名:周前前  刘骊  刘利军  付晓东  黄青松
作者单位:昆明理工大学信息工程与自动化学院 昆明650500;昆明理工大学信息工程与自动化学院 昆明650500;昆明理工大学云南省计算机技术应用重点实验室 昆明650500
基金项目:国家自然科学基金项目(No.61862036,61962030,81860318)、云南省中青年学术和技术带头人后备人才培养计划项目(No.201905C160046)
摘    要:民族服饰图像具有不同民族风格的服装款式、配饰和图案,导致民族服饰图像细粒度检索准确率较低.因此,文中提出细粒度民族服饰图像检索的全局-局部特征提取方法.首先,基于自定义的民族服饰语义标注,对输入图像进行区域检测,分别获得前景、款式、图案和配饰图像.然后在全卷积网络结构的基础上构建多分支的全局-局部特征提取模型,对不同区...

关 键 词:细粒度图像检索  民族服饰图像  全局特征  局部特征  重排序
收稿时间:2020-09-27

Global-Local Feature Extraction Method for Fine-Grained National Clothing Image Retrieval
ZHOU Qianqian,LIU Li,LIU Lijun,FU Xiaodong,HUANG Qingsong. Global-Local Feature Extraction Method for Fine-Grained National Clothing Image Retrieval[J]. Pattern Recognition and Artificial Intelligence, 2021, 34(5): 463-472. DOI: 10.16451/j.cnki.issn1003-6059.202105009
Authors:ZHOU Qianqian  LIU Li  LIU Lijun  FU Xiaodong  HUANG Qingsong
Affiliation:1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500;
2. Computer Technology Application Key Laboratory of Yunnan Province, Kunming University of Science and Technology, Kunming 650500
Abstract:The low accuracy of fine-grained retrieval of national clothing images is caused by different clothing styles, accessories and patterns of national clothing. To address is problem, a global-local feature extraction method for fine-grained clothing image retrieval is proposed. Firstly, the input image is detected to obtain the foreground, styles, accessories and patterns images based on semantic labels of national clothing. Secondly, a multi-branch feature extraction model based on fully convolutional network is constructed to extract features from clothing images in different regions and obtain convolutional features of global, styles, accessories and patterns. Finally, the preliminary retrieval results are obtained by applying a similarity measure to the global features. Then,re-ranking of the result is performed by the local features of top 50 retrieval results and the query image. The final retrieval results are output by the result of re-ranking. The experimental results on the constructed national clothing image dataset show that the proposed method improves the accuracy of national clothing image retrieval effectively.
Keywords:Fine-Grained Image Retrieval  National Clothing Image  Global Feature  Local Feature  Re-ranking  
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