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	使用基于多例学习的启发式SVM算法的图像自动标注
	路晶 ; 马少平 ; Lu Jing ; Ma Shaoping
	2010-06-09 ; 2010-06-09
关键词	图像自动标注 多例学习 支持向量机 整型规划 启发式最优化算法 automatic image annotation Multiple-Instance Learning Support Vector Machine integer program heuristic optimization TP391.41
其他题名	Region-Based Image Annotation Using Heuristic Support Vector Machine in Multiple-Instance Learning
中文摘要	在基于内容的图像检索中,按照图像的语义内容进行自动标注是一个具有挑战性的难题.将解释语义内容的关键词当做图像类别标签可使自动标注问题转化为图像分类问题.对于多数训练数据,关键词仅仅是针对整幅图像来标注的,并不是针对图像中的具体区域.为了克服这个问题,提出了多例学习(MIL)框架下基于支持向量机(SVM)的启发式算法HSVM-MIL.使用迭代的启发式最优化算法来解决多例学习中复杂的整型规划问题,以使分类风险最小化.每次迭代试图改变一个样例的类别以最大化普通SVM的分类间隔.在图像数据库和多例学习的经典数据集MUSK上的实验表明,HSVM-MIL算法具有优良的分类性能.由于该算法针对个体样例的正负分类进行判断,因而能够确定图像区域与关键词之间的对应关系,克服了大多数多例学习算法的缺点.; Content-based image retrieval(CBIR) has been a focal point of multimedia technology since the 1990's,in which automatic image annotation is an important but highly challenging problem.Image annotation is treated as an image classification task in which each class label is considered as a distinct keyword.are usually associated with images instead of individual regions in the training data set.This poses a major challenge for any learning strategy.A new procedure to learn the correspondence between image regions and keywords under Multiple-Instance Learning(MIL) framework is presented as Heuristic Support Vector Machine-based MIL algorithm(HSVM-MIL).It extends the conventional Support Vector Machine(SVM) to the MIL setting by introducing alternative generalizations of the maximum margin used in SVM classification.The learning approach leads to a hard mixed integer program that can be solved iteratively in a heuristic optimization.In each iteration,HSVM-MIL tries to change the class label of only one instance to minimize the classification risk.Because its classification aims at individual image regions,the algorithm can directly estimate the correspondence between image regions and keywords while most MIL algorithms can not do this.Finally the HSVM-MIL algorithm is evaluated on both image annotation data sets and the benchmark MUSK data sets.Compared with other MIL methods,it demonstrates high performance in classification accuracy.; 国家“九七三”重点基础研究发展计划基金项目(2004CB318108); 国家自然科学基金项目(60621062,60503064,60736044); 国家“八六三”高技术研究发展计划基金项目(2006AA01Z141)~~
语种	中文 ; 中文
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/55646]
专题	清华大学
推荐引用方式 GB/T 7714	路晶,马少平,Lu Jing,等. 使用基于多例学习的启发式SVM算法的图像自动标注[J],2010, 2010.
APA	路晶,马少平,Lu Jing,&Ma Shaoping.(2010).使用基于多例学习的启发式SVM算法的图像自动标注..
MLA	路晶,et al."使用基于多例学习的启发式SVM算法的图像自动标注".(2010).

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