Evaluating image segments by applying the description length to sets of superpixels

Image segmentation is a fundamental task in computer vision and a prerequisite for many applications. But what is a good segmentation? One possible answer is given by the segmentation-by-composition framework that defines a good segment as one that can easily be composed by parts of itself. However, this framework is originally based on pixels which causes several problems, among them the need for additional input in form of boundary maps. In this paper, we transform this framework to the domain of superpixels, homogeneous image regions aligning well with object boundaries that can be used as atomic building blocks. At the core of this framework is a score function that quantifies the quality of a given segmentation. We extend this score function to the more general multi-segment case and compute it efficiently for sets of superpixels based on their description length. The score function is solely based on superpixels and does neither require any parameters nor are there thresholds involved. As our secondary contribution and to demonstrate the versatility of the score function for sets of superpixels, we show three applications: salient object detection, fully automatic parameter-free image segmentation, and a combination of both, the automatic segmentation of the most dominant object in an image.