Improving shape retrieval by spectral matching and meta similarity

Authors:
Amir Egozi;Yosi Keller;Hugo Guterman
Affiliations:
Department of Electrical Engineering, Ben-Gurion University, Beer-Sheva, Israel;School of Engineering, Bar Han University, Israel;Department of Electrical Engineering, Ben-Gurion University, Beer-Sheva, Israel
Venue:
IEEE Transactions on Image Processing
Year:
2010

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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 quadratic assignment. By utilizing state-of-the-art shape descriptors and pairwise serialization constraint, we derive a formulation that is resilient to boundary noise, articulations and nonrigid deforma. tionds. This allows both shape matching and retrieval. We also mtro uce 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%.