计算机应用与软件2017，Vol.34Issue(4)：255-259,5.DOI:10.3969/j.issn.1000-386x.2017.04.043
基于度量学习的服装图像分类和检索
CLOTHING IMAGE CLASSIFICATION AND RETRIEVAL BASED ON METRIC LEARNING
包青平 1孙志锋1
作者信息
- 1. 浙江大学电气工程学院 浙江 杭州 310058
- 折叠
摘要
Abstract
On the problem of clothing image classification and retrieval, the general convolutional neural network has limited ability to identify because of diverse patterns and different backgrounds in image.To solve this problem, a convolution neural network method based on metric learning is proposed, in which the metric learning is based on the triplet loss, and the network has three inputs: the reference sample, the positive sample and the negative sample.By means of metric learning, it is possible to reduce the intra-class feature distance and increase the inter-class feature distance, so as to achieve the fine-grained classification.In addition, the images in different backgrounds are input into the training network as positive samples to improve the anti-interference ability.On the problem of clothing retrieval, a fine-grained retrieval method is proposed, which combines features of convolutional layers and fully-connected layers.The experimental results show that the introduction of metric learning can enhance the feature extraction ability of the network and improve the accuracy of classification, and the retrieval based on combined features can ensure the accuracy of the results.关键词
服装/分类/检索/多标签/度量学习Key words
Clothing/Classification/Retrieval/Multi-label/Metric learning分类
信息技术与安全科学引用本文复制引用
包青平,孙志锋..基于度量学习的服装图像分类和检索[J].计算机应用与软件,2017,34(4):255-259,5.
