Segmentation Based Interest Points and Evaluation of Unsupervised Image Segmentation Methods

This paper investigates segmentation based interest points for matching and recognition. We propose two simple methods for extracting features from the segmentation maps, which focus on the boundaries and centres of the gravity of the segments. In addition, this can be considered a novel approach for evaluating unsupervised image segmentation algorithms. Former evaluations aim at estimating segmentation quality by how well resulting segments adhere to the contours separating ground-truth foregrounds from backgrounds and therefore explicitly focus on particular objects of interest. In contrast, we propose to measure the robustness of segmentations by the repeatability of features extracted from segments on images related by various geometric and photometric transformations. Further, our evaluation provides a new insight into suitability of the segmentation methods for generating local features for image retrieval or recognition. Several segmentation methods are evaluated and compared to state-of-the art interest point detectors using the repeatability criteria as well as standard matching and recognition benchmarks.