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From Meaningful Contours to Discriminative Object Shape

Shape is a natural, highly prominent characteristic of objects that human vision utilizes everyday. But despite its expressiveness, shape poses significant challenges for category-level object detection in cluttered scenes: Object form is an emergent property that cannot be perceived locally but becomes only available once the whole object has been detected and segregated from the background. Thus we address the detection of objects and the assembling of their shape simultaneously. A dictionary of meaningful contours is obtained by clustering based on contour co-activation in all training images. We seek a joint, consistent placement of all contours in an image, since placing them independently from another is not reliable due to the emergence of shape. Therefore, the characteristic object shape is learned by discovering spatially consistent configurations of all dictionary contours using maximum margin multiple instance learning. During recognition, objects are detected and their shape is explained simultaneously by optimizing a single cost function. Our approach improves upon the current known best performance on the standard shape benchmarks.


Pradeep Yarlagadda, and Björn Ommer,
From Meaningful Contours to Discriminative Object Shape,
in: ECCV'12, 2012.

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