Bagging stochastic watershed on natural color image segmentation

Abstract

The stochastic watershed is a probabilistic segmentation approach which estimates the probability density of contours of the image from a given gradient. In complex images, the stochastic watershed can enhance insignificant contours. To partially address this drawback, we introduce here a fully unsupervised multi-scale approach including bagging. Re-sampling and bagging is a classical stochastic approach to improve the estimation. We have assessed the performance, and compared to other version of stochastic watershed, using the Berkeley segmentation database.