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Authors: Xingyu Chen 1 ; 2 ; Guangping Zeng 2 ; Jiří Kosinka 1 and Alexandru Telea 3

Affiliations: 1 Bernoulli Institute, Faculty of Science and Engineering, University of Groningen, The Netherlands ; 2 School of Computer and Communication Engineering, University Science and Technology Beijing, Beijing, China ; 3 Utrecht University, The Netherlands

Keyword(s): Content-based Shape Retrieval, Multidimensional Projections, Feature Selection, Visual Analytics.

Abstract: : We present a visual analytics approach for constructing effective visual representations of 3D shape databases as projections of multidimensional feature vectors extracted from their shapes. We present several methods to construct effective projections in which different-class shapes are well separated from each other. First, we propose a greedy heuristic for searching for near-optimal projections in the space of feature combinations. Next, we show how human insight can improve the quality of the constructed projections by iteratively identifying and selecting a small subset features that are responsible for characterizing different classes. Our methods allow users to construct high-quality projections with low effort, to explain these projections in terms of the contribution of different features, and to identify both useful features and features that work adversely for the separation task. We demonstrate our approach on a real-world 3D shape database.

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Paper citation in several formats:
Chen, X.; Zeng, G.; Kosinka, J. and Telea, A. (2020). Visual Exploration of 3D Shape Databases Via Feature Selection. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - IVAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 42-53. DOI: 10.5220/0008950700420053

@conference{ivapp20,
author={Xingyu Chen. and Guangping Zeng. and Ji\v{r}í Kosinka. and Alexandru Telea.},
title={Visual Exploration of 3D Shape Databases Via Feature Selection},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - IVAPP},
year={2020},
pages={42-53},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008950700420053},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - IVAPP
TI - Visual Exploration of 3D Shape Databases Via Feature Selection
SN - 978-989-758-402-2
IS - 2184-4321
AU - Chen, X.
AU - Zeng, G.
AU - Kosinka, J.
AU - Telea, A.
PY - 2020
SP - 42
EP - 53
DO - 10.5220/0008950700420053
PB - SciTePress

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