A Boundary-Fragment-Model for Object Detection

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, c...

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Andreas Opelt, Axel Pinz, Andrew Zisserman
• Keywords: BFM Detector, Class-discriminative Boundary Fragments, Computer Vision, ECCV 2006, Object Class Detection, Object Classes, Salient Image Fragments