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图像分类的随机半监督采样方法 被引量：3

Image Classification:A Random Semi-Supervised Sampling Approach
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摘要 为更好地利用大量未标注图像样本信息来提高分类器性能,提出一种半监督学习的图像分类算法——随机半监督采样(RSSS).该算法采用迭代随机采样方法,每次采样中通过谱聚类估计未标注样本的类别值,使用SVM进行模型学习,逐步优化模型;同时,使用图像的局部空间直方图特征可以有效地结合图像的统计和空间信息,以提高分类准确度.实验结果表明,RSSS算法可以充分利用未标注样本信息提高分类器的性能,并且可以有效地消除几何变换带来的影响. An image classification method is presented based on random semi-supervised sampling （RSSS）. RSSS is an iterative algorithm in which the following two steps alternate till convergence： （1）random semi-supervised sampling; （2）semi-supervised spectral clustering for sample labeling and SVM for model training. RSSS uses local spatial histogram as the image feature and it can combine the image spatial relations with statistical information together. The experiments show that the proposed method can use unlabeled images to improve image classification performance and is not sensitive to image geometrical transform.
作者 韩东峰 朱志良 李文辉
机构地区 东北大学软件学院 吉林大学计算机科学与技术学院
出处 《计算机辅助设计与图形学学报》 EI CSCD 北大核心 2009年第9期1333-1338,共6页 Journal of Computer-Aided Design & Computer Graphics
基金 国家"八六三"高技术研究发展计划(2008AA10Z224) 国家自然科学基金(60872040 60873147) 教育部博士点基金(20060183042) 吉林省科技发展计划基金(20060527)
关键词 图像分类 局部特征 随机半监督采样 谱聚类 SVM image classification local features random semi-supervised sampling spectral clustering SVM
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
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共引文献18

同被引文献32

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引证文献3

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计算机辅助设计与图形学学报
2009年 第9期

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