Global-Local Feature Extraction Method for Fine-Grained National Clothing Image Retrieval

被引：0
|
作者：
Zhou Q. [1 ]
Liu L. [1 ,2 ]
Liu L. [1 ,2 ]
Fu X. [1 ,2 ]
Huang Q. [1 ,2 ]
机构：
[1] Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming
[2] Computer Technology Application Key Laboratory of Yunnan Province, Kunming University of Science and Technology, Kunming
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2021年 / 34卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Fine-grained image retrieval; Global feature; Local feature; National clothing image; Re-ranking;
D O I：
10.16451/j.cnki.issn1003-6059.202105009
中图分类号：
学科分类号：
摘要：
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. © 2021, Science Press. All right reserved.
引用
收藏
页码：463 / 472
页数：9
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