Feature Selection for Enhanced Spectral Shape Comparison
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Date
2010
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
In the context of shape matching, this paper proposes a framework for selecting the Laplacian eigenvalues of 3D shapes that are more relevant for shape comparison and classification. Three approaches are compared to identify a specific set of eigenvalues such that they maximise the retrieval and/or the classification performance on the input benchmark data set: the first k eigenvalues, by varying k over the cardinality of the spectrum; the Hill Climbing technique; and the AdaBoost algorithm. In this way, we demonstrate that the information coded by the whole spectrum is unnecessary and we improve the shape matching results using only a set of selected eigenvalues. Finally, we test the efficacy of the selected eigenvalues by coupling shape classification and retrieval.
Description
@inproceedings{:10.2312/3DOR/3DOR10/031-038,
booktitle = {Eurographics Workshop on 3D Object Retrieval},
editor = {Mohamed Daoudi and Tobias Schreck},
title = {{Feature Selection for Enhanced Spectral Shape Comparison}},
author = {Marini, Simone and Patané, Giuseppe and Spagnuolo, Michela and Falcidieno, Bianca},
year = {2010},
publisher = {The Eurographics Association},
ISSN = {1997-0471},
ISBN = {978-3-905674-22-4},
DOI = {/10.2312/3DOR/3DOR10/031-038}
}