Primitive Shape Recognition via Superquadric Representation using Large Margin Nearest Neighbor Classifier

Ryo Hachiuma, Yuko Ozasa, Hideo Saito

2017

Abstract

It is known that humans recognize objects using combinations and positional relations of primitive shapes. The first step of such recognition is to recognize 3D primitive shapes. In this paper, we propose a method for primitive shape recognition using superquadric parameters with a metric learning method, large margin nearest neighbor (LMNN). Superquadrics can represent various types of primitive shapes using a single equation with few parameters. These parameters are used as the feature vector of classification. The real objects of primitive shapes are used in our experiment, and the results show the effectiveness of using LMNN for recognition based on superquadrics. Compared to the previous methods, which used k-nearest neighbors (76.5%) and Support Vector Machines (73.5%), our LMNN method has the best performance (79.5%).

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Paper Citation


in Harvard Style

Hachiuma R., Ozasa Y. and Saito H. (2017). Primitive Shape Recognition via Superquadric Representation using Large Margin Nearest Neighbor Classifier . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 325-332. DOI: 10.5220/0006153203250332


in Bibtex Style

@conference{visapp17,
author={Ryo Hachiuma and Yuko Ozasa and Hideo Saito},
title={Primitive Shape Recognition via Superquadric Representation using Large Margin Nearest Neighbor Classifier},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={325-332},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006153203250332},
isbn={978-989-758-226-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Primitive Shape Recognition via Superquadric Representation using Large Margin Nearest Neighbor Classifier
SN - 978-989-758-226-4
AU - Hachiuma R.
AU - Ozasa Y.
AU - Saito H.
PY - 2017
SP - 325
EP - 332
DO - 10.5220/0006153203250332