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图像语义分析的多示例学习算法综述
引用本文:李大湘,赵小强,李 娜.图像语义分析的多示例学习算法综述[J].控制与决策,2013,28(4):481-488.
作者姓名:李大湘  赵小强  李 娜
作者单位:1. 西安邮电大学通信与信息工程学院,西安 710121
2. 陕西省法庭科学电子信息实验研究中心图像处理实验室,西安 710121
基金项目:

国家自然科学基金:基于供应链低碳化的企业行为与运营优化决策研究;陕西省教育厅基金

摘    要:多示例学习(MIL)作为第4种机器学习框架,已在图像语义分析中得到了广泛应用.首先介绍MIL的起源、特点、相关概念和数据集;然后以图像语义分析为应用背景,对相关MIL算法进行详细综述,按照算法采用的学习机制对其进行分类,并重点分析了各类算法提出的思路和主要特点;最后,对MIL未来的研究方向作了探讨.

关 键 词:多示例学习  图像分类  图像检索  图像语义分析
收稿时间:2012/7/13 0:00:00
修稿时间:2012/10/15 0:00:00

Survey of MIL algorithms for image semantic analysis
LI Da-xiang,ZHAO Xiao-qiang,LI Na.Survey of MIL algorithms for image semantic analysis[J].Control and Decision,2013,28(4):481-488.
Authors:LI Da-xiang  ZHAO Xiao-qiang  LI Na
Abstract:

Multi-instance learning(MIL) has been recognized as the fourth machine learning framework, and has been widely
used in the image semantic analysis. Firstly, the concepts such as development history, characteristics and many useful testing
datasets of MIL techniques are reviewed. Then, many popular MIL algorithms are also introduced in detail by using realworld
applications based on image semantic analysis. Meanwhile, based on their machine learning mechanisms, related MIL
algorithms are divided into a variety of categories, which highlights the processes and dominant features of different MIL
algorithms. Finally, the trends and possible outputs for further researches are discussed in details.

Keywords:multi-instance learning  image categorization  image retrieval  image semantic analysis
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