An optimization approach to unsupervised hierarchical texture segmentation
We introduce a novel optimization framework for hierarchical data clustering and apply it to the problem of unsupervised texture segmentation. The proposed objective function assesses the quality of an image partitioning simultaneously at different resolution levels and yields a sequence of consistently nested image segmentations. A novel model selection criterion to select significant image structures from various scales is proposed. As an efficient deterministic optimization heuristic a mean-field annealing algorithm is derived.