Scale selection for supervised image segmentation

Abstract

Finding the right scales for feature extraction is crucial for supervised image segmentation based on pixel classification. There are many scale selection methods in the literature; among them the one proposed by Lindeberg is widely used for image structures such as blobs, edges and ridges. Those schemes are usually unsupervised, as they do not take into account the actual segmentation problem at hand. In this paper, we consider the problem of selecting scales, which aims at an optimal discrimination between user-defined classes in the segmentation. We show the deficiency of the classical unsupervised scale selection paradigms and present a supervised alternative. In particular, the so-called max rule is proposed, which selects a scale for each pixel to have the largest confidence in the classification across the scales. In interpreting the classifier as a complex image filter, we can relate our approach back to Lindeberg's original proposal. In the experiments, the max rule is applied to artificial and real-world image segmentation tasks, which is shown to choose the right scales for different problems and lead to better segmentation results.