Image classification using three order statistics in non-Euclidean spaces

This paper presents a novel image classification scheme, named high order statistics based maximum a posterior (HOS-MAP). To bridge the gap between hum an 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 of triplets of neighbor points for each image data. Finally, a maximum a posteriori 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 MAP-HOR scheme.