摘要
针对场景分类方法在室内场景领域的分类精度普遍较低的问题,提出一种融合全局特征和局部特征的多特征室内场景分类的方法.首先,提取场景图像的SIFT局部特征并根据关键点位置进行聚类处理和降维,得到统一维度的SIFT特征矩阵;其次,提取场景图像的PHOG局部特征和Gist全局特征,并与SIFT特征融合在同一特征矩阵中;然后,采用SVM分类器进行场景分类的训练与识别.实验结果表明,相对于单一特征的场景图像分类方法,本文的方法具有更高的分类精度.
In order to improve the recognition accuracy of indoor scene images a method was proposed through fusing the global and local features. First,the SIFT feature of scene images are extracted and the key points of SIFT are clustered in order to obtain the same dimension feature vector. The PCA is employed to reduce the dimension of feature matrix. Second,the PHOG and Gist features are extracted respectively and fused with the SIFT to construct feature matrix. Finally,the SVM is employed to classify the scene images＇ types. The experimental results show that the recognition accuracy of multi-featured fusion is better than those single-featured ones.
出处
《广东工业大学学报》
CAS
2015年第1期75-79,共5页
Journal of Guangdong University of Technology
基金
广东省科技计划项目(2011B040300002)
广东工业大学团队平台重大成果培育基金项目(GDUT2011-10)
关键词
室内
场景分类
单一特征
多特征融合
indoor
scene recognition
single feature
multi-features fusing
