Scale-invariant sampling for supervised image segmentation

Yan Li, Marco Loog

Abstract: Scale invariance is a desirable property for many vision tasks such as image segmentation and classification. One way to achieve such invariance is to collect images containing objects of all scales and then train a classifier. In practice, however, only a finite number of images at a finite number of scales can be collected, and this poses the problem of scale sampling. In this paper, we focus on how to properly sample over scales in order to solve scale-invariant image segmentation. The ideal distributions of images and features in a scale-invariant setting are derived, and their implications for scale sampling and feature extraction are studied. Some basic image segmentation experiments are conducted to examine the sampling rules proposed, which show that it is possible to train a scale-invariant classifier from a single image.