An Efficient Method for Optimizing Segmentation Parameters

Abstract

Segmenting an image into meaningful regions is an important step in many computer vision applications such as facial recognition, target tracking and medical image analysis. Because image segmentation is an ill-posed problem, parameters are needed to constrain the solution to one that is suitable for a given application. For a user, setting parameter values is often unintuitive. We present a method for automating segmentation parameter selection using an efficient search method to optimize a segmentation objective function. Efficiency is improved by utilizing prior knowledge about the relationship between a segmentation parameter and the objective function terms. An adaptive sampling of the search space is created which focuses on areas that are more likely to contain a minimum. When compared to parameter optimization approaches based on genetic algorithm, Tabu search, and multi-locus hill climbing the proposed method was able to achieve equivalent optimization results with an average of 25% fewer objective function evaluations.