A novel similarity measure for compression and classification
Yusuf Öztürk, H. Abut · 2002
In this study we propose a new architecture for texture classification based on pair-wise pixel associations as an extension of the recently developed multivalued recursive network (MAREN) architecture. Maybe more critically we propose a novel similarity measure and classification algorithm to be used with this network. The proposed fidelity criterion has been observed to be tightly coupled with the ubiquitous mean-square error (MSE) distance measure. Both system of associative relationships (SOAR) and MAREN structures can be considered extensions of the associative memory concept frequently used in neural networks. Our proposed similarity measure is based on the principle of directional divergence of interpixel relationships in a given texture and promises a number of advantages over the MSE measure. In this paper, SOAR will be discussed within the framework of a texture classification problem, but we believe it would be very easy to extend to other applications where interpixel relationship is the primary focus.