Improving shape retrieval by spectral matching and meta similarity

Amir Egozi, Yosi Keller, Hugo Guterman

Research output: Contribution to journalArticlepeer-review

60 Scopus citations

Abstract

We propose two computational approaches for improving the retrieval of planar shapes. First, we suggest a geometrically motivated quadratic similarity measure, that is optimized by way of spectral relaxation of a quadratic assignment. By utilizing state-of-the-art shape descriptors and a pairwise serialization constraint, we derive a formulation that is resilient to boundary noise, articulations and nonrigid deformations. This allows both shape matching and retrieval. We also introduce a shape meta-similarity measure that agglomerates pairwise shape similarities and improves the retrieval accuracy. When applied to the MPEG-7 shape dataset in conjunction with the proposed geometric matching scheme, we obtained a retrieval rate of 92.5%.

Original languageEnglish
Article number5378651
Pages (from-to)1319-1327
Number of pages9
JournalIEEE Transactions on Image Processing
Volume19
Issue number5
DOIs
StatePublished - 1 May 2010

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