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:2019,28(3):229-234
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基于深度学习的服装图像检索方法
(浙江理工大学 信息学院, 杭州 310018)
Clothing Image Retrieval Method Based on Deep Learning
(School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China)
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投稿时间:2018-09-28    修订日期:2018-10-23
中文摘要: 为实现面向大规模服装图像集的图像快速精准检索,突破当前常规检索方法的局限性,本文提出了一个新的深度学习模型:Fashion-16服装图像检索模型.采用先分类再类内检索的思想,基于VGG-16模型强大的图像特征提取能力,以卷积神经网络softmax分类器进行分类,对同一类别下采用局部敏感哈希的思想进行近似最近邻的查找,实现了针对服装类别属性的图像检索模型修正.实验结果表明,模型具有良好的稳定性、精确率及检索速度,有其实用价值与研究意义.
Abstract:In order to achieve fast and accurate image retrieval for large-scale clothing image sets and break through the limitations of current conventional retrieval methods, this study proposes a new deep learning model:Fashion-16 clothing image retrieval model. Based on the idea of first classification and intra-class retrieval, based on the powerful image feature extraction ability of VGG-16 model, the convolutional neural network Softmax classifier is used for classification, and the nearest neighbor search is performed for the idea of locally sensitive hashing under the same category. An image retrieval model correction for clothing category attributes is implemented. The experimental results show that the model has good stability, accuracy, and retrieval speed, and has practical value and research significance.
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基金项目:浙江省科技厅(重大)项目(2015C03001)
引用文本:
陈双,何利力,郑军红.基于深度学习的服装图像检索方法.计算机系统应用,2019,28(3):229-234
CHEN Shuang,HE Li-Li,ZHENG Jun-Hong.Clothing Image Retrieval Method Based on Deep Learning.COMPUTER SYSTEMS APPLICATIONS,2019,28(3):229-234

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