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利用编码层特征组合进行场景分类
引用本文:崔崟,段菲,章毓晋. 利用编码层特征组合进行场景分类[J]. 吉林大学学报(工学版), 2013, 0(Z1): 450-454
作者姓名:崔崟  段菲  章毓晋
作者单位:北京航空航天大学电子信息工程学院;清华大学电子工程系
基金项目:国家自然科学基金项目(61171118);教育部高等学校博士学科点专项科研基金项目(SRFDP-20110002110057)
摘    要:为了提高图像场景分类效果,一种有效的方法是将不同特征组合起来,以常用的SIFT特征和归一化颜色直方图(NCH)特征为例研究了不同语义层次上的组合——特征层的组合和编码层的组合以及它们的效果。在几个常用数据库上的实验结果表明,相比简单的组合特征,在特征提取后再进行组合能在降低特征维数的情况下保持分类的效果,而在编码层的特征组合能获得更高的分类准确率。这表明特征组合应尽量在较高的语义层次上进行。

关 键 词:场景分类  词袋模型  特征组合  语义层次

Scene classification based on coding layer feature combination
CUI Yin,DUAN Fei,ZHANG Yu-jin. Scene classification based on coding layer feature combination[J]. Journal of Jilin University:Eng and Technol Ed, 2013, 0(Z1): 450-454
Authors:CUI Yin  DUAN Fei  ZHANG Yu-jin
Affiliation:1.School of Electronic Information Engineering,Beihang University,Beijing 100191,China;2.Department of Electronic Engineering,Tsinghua University,Beijing 100084,China)
Abstract:In order to improve classification performance of image,one efficient method was to combine different features together.The combination in various layers(feature layer and coding layer) was studied with the help of SIFT feature and normalized color histogram(NCH) feature,their effects were also compared.Experiments on several popular datasets show that,compared to combined features,the feature combination after feature extraction can reduce feature dimension while keep the similar classification rate,and the feature combination on coding layer can achieve even higher classification rate.It is concluded that feature combination should be better performed on higher semantic level.
Keywords:scene classification  bag of words  feature combination  semantic level
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