PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Improving Shape retrieval by Spectral Matching and Meta Similarity
Yosi Keller and Amir Agozi
IEEE Transactions on Image Processing Volume accepetd, 2009.

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 non-rigid 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%.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:5829
Deposited By:Yosi Keller
Deposited On:08 March 2010