标题:Visual Features Fusion for Scene Images Classification
作者:Gao Hua; Zhao Chun-xia; Zhang Hao-feng
通讯作者:Hua, G
作者机构:[Gao Hua; Zhao Chun-xia; Zhang Hao-feng] Shandong Univ, Weihai, Peoples R China.
会议名称:International MultiConference of Engineers and Computer Scientists (IMECS 2012)
会议日期:MAR 14-16, 2012
来源:INTERNATIONAL MULTICONFERENCE OF ENGINEERS AND COMPUTER SCIENTIST, IMECS 2012, VOL II
出版年:2012
卷:2196
页码:912-915
关键词:Scene Classification; Robot Navigation; Gaussian Mixture Model (GMM);; Discrete Cosine Transform (DCT); Color Moments
摘要:Scene images classification is one of the most important methods in robot navigation in unstructured environment. In this paper, a scene images classification method based on visual feature fusion is proposed. First, the scene image is divided into fixed-size blocks. Second, computes the DCT coefficients of each blocks and runs a wavelet-like decomposition, means and variances are extracted in each component of the decomposed sub-blanks. Third, color moments are extracted and fused with DCT texture-based features to describe each block. Finally, a Gaussian Mixture Model (GMM) is employed for training and classification.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867482056&partnerID=40&md5=a5f56fc55b7cb504563ed00708692587
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