Similarity Score Fusion by Ranking Risk Minimization for 3D Object Retrieval
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Date
2008
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
In this work, we introduce a score fusion scheme to improve the 3D object retrieval performance. The state of the art in 3D object retrieval shows that no single descriptor is capable of providing fine grain discrimination required by prospective 3D search engines. The proposed fusion algorithm linearly combines similarity information originating from multiple shape descriptors and learns their optimal combination of weights by minimizing the empirical ranking risk criterion. The algorithm is based on the statistical ranking framework [CLV07], for which consistency and fast rate of convergence of empirical ranking risk minimizers have been established. We report the results of ontology-driven and relevance feedback searches on a large 3D object database, the Princeton Shape Benchmark. Experiments show that, under query formulations with user intervention, the proposed score fusion scheme boosts the performance of the 3D retrieval machine significantly.
Description
@inproceedings{10.2312:3DOR/3DOR08/041-048,
booktitle = {Eurographics 2008 Workshop on 3D Object Retrieval},
editor = {Stavros Perantonis and Nikolaos Sapidis and Michela Spagnuolo and Daniel Thalmann},
title = {{Similarity Score Fusion by Ranking Risk Minimization for 3D Object Retrieval}},
author = {Akgül, Ceyhun Burak and Sankur, Bülent and Yemez, Yücel and Schmitt, Francis},
year = {2008},
publisher = {The Eurographics Association},
ISSN = {1997-0463},
ISBN = {978-3-905674-05-7},
DOI = {10.2312/3DOR/3DOR08/041-048}
}