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in DAGM’04 Pattern Recognition Symposium , T¨ubingen, Germany, Aug 2004. Scale-Invariant Object Categorization using a Scale-Adaptive Mean-Shift Search Bastian Leibe and Bernt Schiele Perceptual Computing and Computer Vision Group, ETH Zurich, Switzerland [email protected] , http://www.vision.ethz.ch/leibe Multimodal Interactive Systems, TU Darmstadt, Germany [email protected] Abstract. 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 real- world 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 ap- proach 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. 1 Introduction Many current object detection methods deal with the scale problem by performing an exhaustive search over all possible object positions and scales [17–19]. This exhaus- tive search imposes severe constraints, both on the detector’s computational complexity and on its discriminance, since a large number of potential false positives need to be excluded. An opposite approach is to let the search be guided by image structures that give cues about the object scale. In such a system, an initial interest point detector tries to find structures whose extend can be reliably estimated under scale changes. These structures are then combined to derive a comparatively small number of hypotheses for object locations and scales. Only those hypotheses that pass an initial plausibility test need to be examined in detail. In recent years, a range of scale-invariant interest point detectors have become available which can be used for this purpose [13–15,10]. In this paper, we apply this idea to extend the method from [12,11]. This method has recently been demonstrated to yield excellent object detection results and high ro- bustness to occlusions [11]. However, it has so far only been defined for categorizing objects at a known scale. In practical applications, this is almost never the case. Even in scenarios where the camera location is relatively fixed, objects of interest may still exhibit scale changes of at least a factor of two simply because they occur at different distances to the camera. Scale invariance is thus one of the most important properties for any system that shall be applied to real-world scenarios without human intervention.
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