Spaces and subspaces of images for recognition

In this paper we study and compare the recognition performance of subspaces in two different spaces, namely the image space and spectral histogram space. In image space, each image is represented as a long vector and in the spectral histogram space, each image is represented by its histograms of the convolved images with a chosen bank of filters. Spectral histogram space is a nonlinear transformation of the image space. First principal components and independent components in the spaces are studied. Then we study different subspaces by connecting the known subspaces through geodesic curves in the projection space. Our preliminary results show the recognition performance depends more on which space to use than the different subspaces in a given space. This suggests the need to study different spaces for recognition purpose.