成婷,张扩,续欣莹

• 1:太原理工大学信息工程学院

摘要(Abstract)：

传统的行人检测方法为静态特征提取方法,而实际的数据是动态变化的,在增加图像集之后,特征集需要重新动态更新。因此,基于信息观的角度,将粗糙集与图像的特征提取方式相结合,引出不一致邻域的概念,提出一种基于增量式场景图像特征选择的邻域粗糙集约简算法研究,该算法运用新增图像样本集及其不一致邻域的样本进行特征提取,有效地避免了不必要的约简,从而快速求得特征集。最后通过实验验证了算法的有效性和高效性。

关键词(KeyWords)： 不一致邻域;粗糙集;属性约简;信息熵;增量式学习;图像特征提取

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金(61503271);; 山西省自然科学基金(2014011018-2)~~

作者(Author): 成婷,张扩,续欣莹

DOI: 10.16652/j.issn.1004-373x.2018.21.013

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