Object categorization using bone graphs

  • Authors:

  • Diego Macrini;Sven Dickinson;David Fleet;Kaleem Siddiqi

  • Affiliations:

  • School of Information Technology and Engineering, University of Ottawa, Colonel By, 800 King Edward Av, Room B407, Ottawa, Ontario, Canada K1N 6N;Department of Computer Science, University of Toronto, 6 King's College Rd, Room PT 283, Toronto, Ontario, Canada M5S 3H5;Department of Computer Science, University of Toronto, 6 King's College Rd, Room PT 283, Toronto, Ontario, Canada M5S 3H5;McGill University, Rm 318, McConnell Eng., 3480 University Street, Montreal, Quebec, Canada H3A 2A7

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2011

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Abstract

The bone graph (Macrini et al., in press, 2008) [23,25] is a graph-based medial shape abstraction that offers improved stability over shock graphs and other skeleton-based descriptions that retain unstable ligature structure. Unlike the shock graph, the bone graph's edges are attributed, allowing a richer specification of relational information, including how and where two medial parts meet. In this paper, we propose a novel shape matching algorithm that exploits this relational information. Formulating the problem as an inexact directed acyclic graph matching problem, we extend a leading bipartite graph-based algorithm for matching shock graphs (Siddiqi et al., 1999) [41]. In addition to accommodating the relational information, our new algorithm is better able to enforce hierarchical and sibling constraints between nodes, resulting in a more general and more powerful matching algorithm. We evaluate our algorithm with respect to a competing shock graph-based matching algorithm, and show that for the task of view-based object categorization, our algorithm applied to bone graphs outperforms the competing algorithm. Moreover, our algorithm applied to shock graphs also outperforms the competing shock graph matching algorithm, demonstrating the generality and improved performance of our matching algorithm.