Segmentation by Adaptive Prediction and Region Merging

This paper presents a segmentation technique based on pre- diction and adaptive region merging. While many techniques for segmentation exist, few of them are suited for the segmentation of natural images containing regular textures defined on non-rectangular segments. In this paper, we propose a description of regions based on a deconvolution algorithm whose purpose is to remove the influence of the shape on region contents. The decoupling of shape and texture information is achieved either by adapting waveforms to the segment shape, which is a time-consuming task that needs to be repeated for each segment shape, or by the extrapolation of a signal to fit a rectangular window, which is the chosen path. The deconvolution algorithm is the key of a new segmentation technique that uses extrapolation as a prediction of neighbouring regions. When the prediction of a region fits the actual content of a connected region rea- sonably well, both regions are merged. The segmentation process starts with an over-segmented image. It progressively merges neighbouring re- gions whose extrapolations fit according to an energy criterion. After each merge, the algorithm updates the values of the merging criterion for regions connected to the merged region pair. It stops when no fur- ther gain is achieved in merging regions or when mean values of adjacent regions are too different. Simulation results indicate that, although our technique is tailored for natural images containing periodic signals and flat regions, it is in fact usable for a large set of natural images.