Object class segmentation using reliable regions

Abstract

Image segmentation is increasingly used for object recognition. The advantages of segments are numerous: a natural spatial support to compute features, reduction in the number of hypothesis to test, region shape itself can be a useful feature, etc. Since segmentation is brittle, a popular remedy is to integrate results over multiple segmentations of the scene. In previous work, usually all the regions in multiple segmentations are used. However, a typical segmentation algorithm often produces generic regions lacking discriminating features. In this work we explore the idea of finding and using only the regions that are reliable for detection. The main step is to cluster feature vectors extracted from regions and deem as unreliable any clusters that belong to different classes but have a significant overlap. We use a simple nearest neighbor classifier for object class segmentation and show that discarding unreliable regions results in a significant improvement. © 2011 Springer-Verlag Berlin Heidelberg.