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基于深度学习的图像自动标注算法
杨阳; 张文生
Source Publication数据采集与处理
2015
Volume30Issue:1Pages:88-98
Abstract图像的自动标注是图像检索领域一项基础而又富有挑战性的任务。当前,机器学习算法在该领域取得了一定的进展。深度学习算法自提出以来在图像和文本识别领域取得了巨大的成功,它可以作为一种解决“语义鸿沟”问题的有效方法。图像标注问题可以分解为基于图像与标签相关关系的基本图像标注和基于标注词汇共生关系的标注改善两个过程。文中将基本图像标注问题视为一个多标记学习问题,图像的标签先验知识作为深度神经网络的监督信息。在得到基本标注词汇的基础上,利用原始图像标签词汇的依赖关系与先验分布改善了图像的标注结果。最后所提出的改进的深度学习模型应用于Corel和ESP图像数据集,验证了该模型框架及所提出解决方案的有效性。
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
DOI10.16337/j.1004-9037.2015./01.008
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15507
Collection精密感知与控制研究中心_人工智能与机器学习
Corresponding Author张文生
Affiliation中国科学院自动化研究所
Recommended Citation
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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