Bag-of-feature-graphs: a new paradigm for non-rigid shape retrieval

T Hou, X Hou, M Zhong, H Qin - Proceedings of the 21st …, 2012 - ieeexplore.ieee.org
T Hou, X Hou, M Zhong, H Qin
Proceedings of the 21st International Conference on Pattern …, 2012ieeexplore.ieee.org
This paper advocates a new paradigm, called bag-of-feature-graphs (BoFG), for non-rigid
shape retrieval. It represents a shape by constructing graphs among its features, which
significantly reduces the number of points involved in computation. Given a vocabulary of
geometric words, for each word the BoFG builds a graph that records spatial information of
features, weighted by their similarities to this word. This eliminates unlikely points in a word
category, during shape comparison. Feature graphs are governed by their affinity matrices of …
This paper advocates a new paradigm, called bag-of-feature-graphs (BoFG), for non-rigid shape retrieval. It represents a shape by constructing graphs among its features, which significantly reduces the number of points involved in computation. Given a vocabulary of geometric words, for each word the BoFG builds a graph that records spatial information of features, weighted by their similarities to this word. This eliminates unlikely points in a word category, during shape comparison. Feature graphs are governed by their affinity matrices of weighted heat kernels, whose eigenvalues form a concise shape descriptor. Evaluations of the proposed method are conducted via quantitative measurements. The results demonstrate that the BoFG has competitive precisions w.r.t. state-of-the-art methods, and is much faster to compute.
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