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| Global-Local Feature Extraction Method for Fine-Grained National Clothing Image Retrieval |
| ZHOU Qianqian1, LIU Li1,2, LIU Lijun1,2, FU Xiaodong1,2, HUANG Qingsong1,2 |
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 |
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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.
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Received: 27 September 2020
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| Fund:National Natural Science Foundation of China(No.61862036,61962030,81860318), Young Academic and Tech-nical Leader Candidate Program of Yunnan Province(No.201905C160046) |
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Corresponding Authors:
LIU Li, Ph.D., professor. Her research interests include computer-aided design and computer graphics, image processing and computer vision.
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| About author:: ZHOU Qianqian, master student. His research interests include computer vision and image processing.FU Xiaodong, Ph.D., professor. His research interests include services computing and decision theory and technology.LIU Lijun, Ph.D., associate professor. His research interests include image proce-ssing, cloud computing and information retrieval.HUANG Qingsong, master, professor. His research interests include machine learning, data mining and intelligent information system. |
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2021, Vol. 34 
