标题：Visual Features Fusion for Scene Images Classification
作者：Gao Hua; Zhao Chun-xia; Zhang Hao-feng
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
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.