In this work we demonstrate that a recently introduced shape descriptol; the "shape context", can be used to quickly prune a search for similar shapes. Our representation for a shape is a discrete set of n points sampledfrom its internal and external contours. For each of these points, the shape context is a histogram of the relative positions of the n -1 remaining points. We present two methods for rapid shape retrieval: one that does comparisons based on a small number of shape contexts and another that uses vector quantization in the space of shape contexts. We verifr the discriminative power of these methods with tests on the Columbia (COIL-100) 3 0 object database and the Snodgrass and Vanderwart line drawings. The shape contextbased methods are shown to quickly produce an accurate shortlist of candidates suitable for a more exact matching engine in spite of pose variation and occlusion. .
In REF , efficient retrieval is attained by pruning based on comparisons of a small subset of shape context features, and also using vector quantization.
Shape contexts enable efficient retrieval of similar shapes
Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001
Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001
Computer Science