Abstract
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.
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© 2011 Springer-Verlag Berlin Heidelberg
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Li, C., Gao, C., Xing, S., Hamza, A.B. (2011). Fast Shape Re-ranking with Neighborhood Induced Similarity Measure. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23672-3_32
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DOI: https://doi.org/10.1007/978-3-642-23672-3_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23671-6
Online ISBN: 978-3-642-23672-3
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