Abstract
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 scheme first builds dissimilarity representations on a non-Euclidean space. Then, the information of dissimilarity increment distribution of each image category is achieved based on high-order statistics of triplets of neighbor points for each image data. Finally, a MAP algorithm with the information of Gaussian Mixture Model and triplet-dissimilarity increment distribution is adopted to estimate the relevance between each image category in the database and each input image. Experimental results on a general-purpose image database demonstrate the effectiveness and efficiency of the proposed HOS-MAP scheme.
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