Automatic Selection of Stochastic Watershed Hierarchies

TL;DR

This paper introduces an automatic segmentation method that selects optimal stochastic watershed hierarchies and cut levels for image simplification, improving segmentation quality across various real-life image datasets.

Contribution

It proposes a novel automatic selection strategy for stochastic watershed hierarchies and cut levels based on evaluation scores, enhancing image segmentation accuracy.

Findings

Automatically selects the best hierarchy and cut level for segmentation.

Improves segmentation quality on multiple real-life datasets.

Demonstrates advantages over existing methods.

Abstract

The segmentation, seen as the association of a partition with an image, is a difficult task. It can be decomposed in two steps: at first, a family of contours associated with a series of nested partitions (or hierarchy) is created and organized, then pertinent contours are extracted. A coarser partition is obtained by merging adjacent regions of a finer partition. The strength of a contour is then measured by the level of the hierarchy for which its two adjacent regions merge. We present an automatic segmentation strategy using a wide range of stochastic watershed hierarchies. For a given set of homogeneous images, our approach selects automatically the best hierarchy and cut level to perform image simplification given an evaluation score. Experimental results illustrate the advantages of our approach on several real-life images datasets.