TY - GEN
T1 - Primitive shape recognition via superquadric representation using large margin nearest neighbor classifier
AU - Hachiuma, Ryo
AU - Ozasa, Yuko
AU - Saito, Hideo
N1 - Funding Information:
This work was partially supported by MEXT/JSPS Grant-in-Aid for Scientific Research(S) 24220004, and JST CREST Intelligent Information Processing Systems Creating Co-Experience Knowledge and Wisdom with Human-Machine Harmonious Collaboration
Publisher Copyright:
Copyright © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
PY - 2017
Y1 - 2017
N2 - 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%).
AB - 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%).
KW - 3D shape primitives
KW - Large margin nearest neighbor
KW - Primitive shape recognition
KW - Superquadrics
UR - http://www.scopus.com/inward/record.url?scp=85047869187&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047869187&partnerID=8YFLogxK
U2 - 10.5220/0006153203250332
DO - 10.5220/0006153203250332
M3 - Conference contribution
AN - SCOPUS:85047869187
T3 - VISIGRAPP 2017 - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
SP - 325
EP - 332
BT - VISAPP
A2 - Imai, Francisco
A2 - Tremeau, Alain
A2 - Braz, Jose
PB - SciTePress
T2 - 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2017
Y2 - 27 February 2017 through 1 March 2017
ER -