From local features to global shape constraints: Heterogeneous matching scheme for recognizing objects under serious background clutter

Abstract

Object recognition in computer vision is the task to categorize images based on their content. With the absence of background clutter in images high recognition performance can be achieved. In this paper we show how the recognition performance is improved even with a high impact of background clutter and without additional information about the image. For this task we segment the image into patches and learn a geometric structure of the object. In evaluations we first show that our system is of comparable performance to other state-of-the-art system and that for a difficult dataset the recognition performance is improved by 13.31%. © 2011 Springer-Verlag Berlin Heidelberg.