Matrix correlation distance for 2D image classification

In the field of visual information processing, there have been active studies on the efficient representation of visual data, such as local feature descriptors and tensor subspace analysis. Though these methods give a representation using matrix features, current methods for classification are mainly designed for 1D vector data, which may lead to loss of information included in 2D matrix data. To solve the problem, we propose a matrix correlation distance for 2D image data by extending the correlation distance for random vectors. Through a number of computational experiments on image data with various representations, we compare the performance of the proposed measure with conventional distances.