A boundary-fragment-model for object detection

  • Authors:

  • Andreas Opelt;Axel Pinz;Andrew Zisserman

  • Affiliations:

  • Vision-based Measurement Group, Inst. of El. Measurement and Meas. Sign. Proc., University of Technology, Graz, Austria;Vision-based Measurement Group, Inst. of El. Measurement and Meas. Sign. Proc., University of Technology, Graz, Austria;Visual Geometry Group, Department of Engineering Science, University of Oxford

  • Venue:
  • ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
  • Year:
  • 2006

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Abstract

The objective of this work is the detection of object classes, such as airplanes or horses. Instead of using a model based on salient image fragments, we show that object class detection is also possible using only the object's boundary. To this end, we develop a novel learning technique to extract class-discriminative boundary fragments. In addition to their shape, these “codebook” entries also determine the object's centroid (in the manner of Leibe et al. [19]). Boosting is used to select discriminative combinations of boundary fragments (weak detectors) to form a strong “Boundary-Fragment-Model” (BFM) detector. The generative aspect of the model is used to determine an approximate segmentation. We demonstrate the following results: (i) the BFM detector is able to represent and detect object classes principally defined by their shape, rather than their appearance; and (ii) in comparison with other published results on several object classes (airplanes, cars-rear, cows) the BFM detector is able to exceed previous performances, and to achieve this with less supervision (such as the number of training images).