Image classification using pseudo power signatures

Segmentation and classification are important problems with applications in areas like textural analysis and pattern recognition. This paper describes a single-stage approach to solve the image segmentation/classification problem down to the pixel level, using energy density functions based on the wavelet transform. The energy density functions obtained, called pseudo power signatures, are essentially functions of the scale and orientation and are obtained using separable approximations to the 2-D wavelet transform. A significant advantage of these representations is that they are invariant to signal magnitude, and spatial location within the object of interest. Further, they lend themselves to fast and simple classification routines. We provide a complete formulation of the signature determination problem for 2-D, and propose an effective, albeit simple, technique based on a tensor singular value analysis, to solve the problem. We also present an efficient computational algorithm, and a simulation result reflecting the strengths and limitations of this approach