Image Classification Via Learning Dissimilarity Measure In Non-Euclidean Spaces

摘要：

　　This paper presents a novel image classification scheme,named high order statistics based maximum a posterior(HOS-MAP).To bridge the gap between human judgment and machine intelligence,this framework first builds dissimilarity representations in a modified pseudo-Euclidean space.Then,the information of the dissimilarity increments distribution of each category is achieved based on high-order statistics(HOS)of triplets of neighbor points for each image data,as opposed to typical pair-wise measures,such as the Euclidean distance.Finally,a maximum a posteriori(MAP)algorithm with the information of Gaussian Mixture Model and triplet-dissimilarity increments distribution is adopted to estimate the relevance of each category in the database for each input new image.Experimental results on a general-purpose image database demonstrate that effectiveness and efficiency of the proposed HOS-MAP scheme.

关键词： Dissimilarity Increments Distribution High-Order Statistics Maximum A Posteriori Gaussian Mixture Model Non-Euclidean Space

作者: CHEN Lingling ZHU Songhao LI Zhuofan HU Juanjuan

作者单位: School of Automatic,Nanjing University of Post and Telecommunications,Nanjing,210046

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 4626-4630

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）