GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Abstraction of man-made shapes
ACM SIGGRAPH Asia 2009 papers
A structured learning approach to attributed graph embedding
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Bone graphs: Medial shape parsing and abstraction
Computer Vision and Image Understanding
A generic framework for median graph computation based on a recursive embedding approach
Computer Vision and Image Understanding
Object categorization using bone graphs
Computer Vision and Image Understanding
Skeleton Search: Category-Specific Object Recognition and Segmentation Using a Skeletal Shape Model
International Journal of Computer Vision
Shape abstraction through multiple optimal solutions
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Co-abstraction of shape collections
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Complexity of computing distances between geometric trees
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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Learning a class prototype from a set of exemplars is an important challenge facing researchers in object categorization. Although the problem is receiving growing interest, most approaches assume a one-to-one correspondence among local features, restricting their ability to learn true abstractions of a shape. In this paper, we present a new technique for learning an abstract shape prototype from a set of exemplars whose features are in many-to-many correspondence. Focusing on the domain of 2D shape, we represent a silhouette as a medial axis graph whose nodes correspond to "parts” defined by medial branches and whose edges connect adjacent parts. Given a pair of medial axis graphs, we establish a many-to-many correspondence between their nodes to find correspondences among articulating parts. Based on these correspondences, we recover the abstracted medial axis graph along with the positional and radial attributes associated with its nodes. We evaluate the abstracted prototypes in the context of a recognition task.