| 基于EMD-CkNN多示例学习算法的图像分类 |
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| 引用本文： | 李大湘.基于EMD-CkNN多示例学习算法的图像分类[J].光电子．激光,2010(2):303-306. |
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| 作者姓名： | 李大湘 |
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| 作者单位： | 西北大学信息科学与技术学院;西北工业大学电子信息学院; |
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| 基金项目： | 教育部新世纪优秀人才支持计划资助项目(NCET-07-0693) |
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| 摘 要： | 针对自然图像场景分类问题,根据Citation-kNN算法思想,提出一种新的基于多示例学习(MIL)的图像分类方法。将整个图像当作多示例包,图像分割的区域当作包中的示例,在度量图像包间的相似距离时,利用改进的推土机距离(EMD)代替Citation-KNN算法中的最小Hausdorff距离(minHD),用于图像分类。在Corel图像库上的对比实验结果表明,分类准确率更高。
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| 关 键 词： | 多示例学习(MIL) 图像分类 推土机距离(EMD) |
Image categorization based on EMD-CkNN multi-instance learning algorithm |
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| LI Da-xiang.Image categorization based on EMD-CkNN multi-instance learning algorithm[J].Journal of Optoelectronics·laser,2010(2):303-306. |
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| Authors: | LI Da-xiang |
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| Institution: | 1(1.School of Information Science and Technology,Northwest University,Xi′an 710069,China;2.School of Electronics Information,Northwestern Polytechnical University,Xi′an 710072,China) |
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| Abstract: | Focusing on the problem of natural image scene classification and according to the idea of Citation-kNN algorithm,a new image categorization method is proposed based on multi-instance learning.This method regards the whole image as a multi-instance bag and the segmented regions as instances in this bag.When measuring the similarity between different bags,an improved earth mover distance instead of the minimum Hausdorff distance used in the Citation-kNN multi-instance learning algorithm is used for image cat... |
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| Keywords: | multi-instance learning(MIL) image categorization earth mover′s distance(EMD) |
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