Mean-shift segmentation with wavelet-based bandwidth selection
Recently, various non-linear techniques for segmentation have been proposed based on non-parametric density estimation. These approaches model image data as clusters of pixels in the combined range-domain space, using kernel based techniques to represent the underlying, multi-modal Probability Density Function (PDF). In Mean-shift based segmentation, pixel clusters or image segments are identified with unique modes of the multi-modal PDF by mapping each pixel to a mode using a convergent, iterative process. The advantages of such approaches include flexible modeling of the image and noise processes and consequent robustness in segmentation. An important issue is the automatic selection of scale parameters a problem far from satisfactorily addressed. In this paper, we propose a regression-based model which admits a realistic framework to choose scale parameters. Results on real images are presented.