Contour Context Selection for Object Detection: A Set-to-Set Contour Matching Approach

Abstract. We introduce a shape detection framework called Contour Context Selection for detecting objects in cluttered images using only one exemplar. Shape based detection is invariant to changes of object appearance, and can reason with geometrical abstraction of the object. Our approach uses salient contours as integral tokens for shape matching. We seek a maximal, holistic matching of shapes, which checks shape features from a large spatial extent, as well as long-range contextual relationships among object parts. This amounts to finding the correct figure/ground contour labeling, and optimal correspondences between control points on/around contours. This removes accidental alignments and does not hallucinate objects in background clutter, without negative training examples. We formulate this task as a set-to-set contour matching problem. Naive methods would require searching over 'exponentially' many figure/ground contour labelings. We simplify this task by encoding the ...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Qihui Zhu, Liming Wang, Yang Wu, Jianbo Shi
• Keywords: Computer Vision, Contour Figure/ground Variables, Contour Matching Problem, Correct Figure/ground Contour, ECCV 2008, Framework Called Contour, Shape Matching