Scale Invariant Object Categorization Using a Scale-Adaptive Mean-Shift Search

The goal of our work is object categorization in real-world scenes. That is, given a novel image we want to recognize and localize unseen-before objects based on their similarity to a learned object category. For use in a realworld system, it is important that this includes the ability to recognize objects at multiple scales. In this paper, we present an approach to multi-scale object categorization using scale-invariant interest points and a scale-adaptive Mean-Shift search. The approach builds on the method from [12], which has been demonstrated to achieve excellent results for the single-scale case, and extends it to multiple scales. We present an experimental comparison of the influence of different interest point operators and quantitatively show the method’s robustness to large scale changes.

Awarded the main prize of the German Pattern Recognition Society (DAGM Best Paper Award)