From contours to regions: An empirical evaluation

We propose a generic grouping algorithm that constructs a hierarchy of regions from the output of any contour detector. Our method consists of two steps, an oriented watershed transform (OWT) to form initial regions from contours, followed by construction of an ultra-metric contour map (UCM) defining a hierarchical segmentation. We provide extensive experimental evaluation to demonstrate that, when coupled to a high-performance contour detector, the OWT-UCM algorithm produces state-of-the-art image segmentations. These hierarchical segmentations can optionally be further refined by user-specified annotations.

1. Introduction

Applications such as object recognition [34], [25], [1], [1]9 and monocular inference of 3D structure [20], [41] have led to a renewed interest in algorithms for automatic segmentation of an image into closed regions. Segments come with their own scale estimates and provide natural domains for computing features used in recognition. Many visual tasks can also benefit from the reduction in complexity achieved by transforming an image with millions of pixels into a few hundred or thousand “superpixels” [39].