CASIA OpenIR  > 精密感知与控制研究中心  > 人工智能与机器学习
杨阳; 张文生
Source Publication数据采集与处理
Other AbstractImage auto-annotation is a basic and challenge task in the image retrieval work. The traditional machine learning methods have got a lot achievement in this field. The deep learning algorithm has achieved great success in image and text learning work since it has been presented, so it can be an efficiency method to sole the semantic gap problems. Image auto-annotation can be decomposed into two steps: basic image auto-annotation based on the relationship between image and tag, and annotation enhanced based on the mutual information of the tags. In this article, the basic image auto-annotation is viewed as a multi-labelled problem, so the prior knowledge of the tags can be used as the supervise information of the deep neural network. After the image tags got, the dependent relationship of the tags is used to improve the annotation result. At the end, this model have been tested in Corel and ESP dataset, and been proved that this method can efficiently solve the image auto-annotation problems.
Keyword机器学习 深度学习 神经网络 图像自动标注
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Document Type期刊论文
Corresponding Author张文生
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GB/T 7714
杨阳,张文生. 基于深度学习的图像自动标注算法[J]. 数据采集与处理,2015,30(1):88-98.
APA 杨阳,&张文生.(2015).基于深度学习的图像自动标注算法.数据采集与处理,30(1),88-98.
MLA 杨阳,et al."基于深度学习的图像自动标注算法".数据采集与处理 30.1(2015):88-98.
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