Supervised Texture Segmentation by Maximising Conditional Likelihood

Supervised segmentation of piecewise-homogeneous image textures using a modified conditional Gibbs model with multiple pairwise pixel interactions is considered. The modification takes into account that interregion interactions are usually different for the training sample and test images. Parameters of the model learned from a given training sample include a characteristic pixel neighbourhood specifying the interaction structure and Gibbs potentials giving quantitative strengths of the pixelwise and pairwise interactions. The segmentation is performed by approaching the maximum conditional likelihood of the desired region map provided that the training and test textures have similar conditional signal statistics for the chosen pixel neighbourhood. Experiments show that such approach is more efficient for regular textures described by different characteristic long-range interactions than for stochastic textures with overlapping close-range neighbourhoods. 1 Center for Image Technology and Robotics Tamaki Campus, The University of Auckland, Auckland, New Zealand. [email protected] You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the CITR Tamaki web site under terms that include this permission. All other rights are reserved by the author(s). Supervised Texture Segmentation by Maximising Conditional Likelihood