In this paper, we address the shape retrieval problem by casting it into the task of identifying "authority" nodes in an inferred similarity graph and also by re-ranking the shapes. The main idea is that the average similarity between a node and its neighboring nodes takes into account the local distribution and therefore helps modify the neighborhood edge weight, which guides the re-ranking. The proposed approach is evaluated on both 2D and 3D shape datasets, and the experimental results show that the proposed neighborhood induced similarity measure significantly improves the shape retrieval performance. © 2011 Springer-Verlag.
CITATION STYLE
Li, C., Gao, C., Xing, S., & Hamza, A. B. (2011). Fast shape re-ranking with neighborhood induced similarity measure. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6854 LNCS, pp. 261–268). https://doi.org/10.1007/978-3-642-23672-3_32
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