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%).
| Original language | English |
|---|---|
| Title of host publication | VISAPP |
| Publisher | SciTePress |
| Pages | 325-332 |
| Number of pages | 8 |
| Volume | 5 |
| ISBN (Electronic) | 9789897582264 |
| Publication status | Published - 2017 Jan 1 |
| Event | 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2017 - Porto, Portugal Duration: 2017 Feb 27 → 2017 Mar 1 |
Other
| Other | 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2017 |
|---|---|
| Country | Portugal |
| City | Porto |
| Period | 17/2/27 → 17/3/1 |
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Keywords
- 3D shape primitives
- Large margin nearest neighbor
- Primitive shape recognition
- Superquadrics
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
- Artificial Intelligence
Cite this
Primitive shape recognition via superquadric representation using large margin nearest neighbor classifier. / Hachiuma, Ryo; Ozasa, Yuko; Saito, Hideo.
VISAPP. Vol. 5 SciTePress, 2017. p. 325-332.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Primitive shape recognition via superquadric representation using large margin nearest neighbor classifier
AU - Hachiuma, Ryo
AU - Ozasa, Yuko
AU - Saito, Hideo
PY - 2017/1/1
Y1 - 2017/1/1
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
M3 - Conference contribution
AN - SCOPUS:85047869187
VL - 5
SP - 325
EP - 332
BT - VISAPP
PB - SciTePress
ER -