Abstract
| Original language | English |
|---|---|
| Title of host publication | International Conference on Neural Information Processing (ICONIP 2013) |
| Subtitle of host publication | Lecture Notes in Computer Science |
| Editors | Minho Lee, Akira Hirose, Zeng-Guang Hou, Rhee Man Kil |
| Place of Publication | Germany |
| Publisher | Springer |
| Pages | 384-391 |
| Number of pages | 8 |
| Volume | 8228 |
| ISBN (Electronic) | 9783642420511 |
| ISBN (Print) | 9783642420504 |
| DOIs | |
| Publication status | Published - 2013 |
| Event | 20th International Conference on Neural Information Processing (ICONIP 2013) - Daegu, Daegu, Korea, Republic of Duration: 3 Nov 2013 → 7 Nov 2013 |
Conference
| Conference | 20th International Conference on Neural Information Processing (ICONIP 2013) |
|---|---|
| Abbreviated title | ICONIP 2013 |
| Country | Korea, Republic of |
| City | Daegu |
| Period | 3/11/13 → 7/11/13 |
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Improved HOG Descriptors in Image Classification with CP Demonstration. / Vo, Tan; TRAN, Dat; MA, Wanli; NGUYEN, Khoa.
International Conference on Neural Information Processing (ICONIP 2013): Lecture Notes in Computer Science. ed. / Minho Lee; Akira Hirose; Zeng-Guang Hou; Rhee Man Kil. Vol. 8228 Germany : Springer, 2013. p. 384-391.Research output: A Conference proceeding or a Chapter in Book › Conference contribution
TY - GEN
T1 - Improved HOG Descriptors in Image Classification with CP Demonstration
AU - Vo, Tan
AU - TRAN, Dat
AU - MA, Wanli
AU - NGUYEN, Khoa
PY - 2013
Y1 - 2013
N2 - Histogram of Oriented Gradients (HOG) has been widely used in computer vision as feature descriptors for detecting objects in scenes. We present in this paper a new approach to HOG in image classification that will provide an opportunity to explore new ways to improve the effectiveness of HOG image descriptors. We investigate applying tensor decomposition on HOG descriptors then using them as image features to build image models using support vector machine. The aim of this approach is to produce a more robust and compact version of HOG features. An image classification experiment is performed to evaluate the effectiveness of this approach as well as to identify all ideal parameter values involved. Experimental results show a good improvement in image classification rate for the proposed approach.
AB - Histogram of Oriented Gradients (HOG) has been widely used in computer vision as feature descriptors for detecting objects in scenes. We present in this paper a new approach to HOG in image classification that will provide an opportunity to explore new ways to improve the effectiveness of HOG image descriptors. We investigate applying tensor decomposition on HOG descriptors then using them as image features to build image models using support vector machine. The aim of this approach is to produce a more robust and compact version of HOG features. An image classification experiment is performed to evaluate the effectiveness of this approach as well as to identify all ideal parameter values involved. Experimental results show a good improvement in image classification rate for the proposed approach.
KW - Age and Gender Classification
KW - Tensor Decomposition
KW - histogram of oriented gradients
UR - https://link.springer.com/chapter/10.1007/978-3-642-42051-1_48
U2 - 10.1007/978-3-642-42051-1_48
DO - 10.1007/978-3-642-42051-1_48
M3 - Conference contribution
SN - 9783642420504
VL - 8228
SP - 384
EP - 391
BT - International Conference on Neural Information Processing (ICONIP 2013)
A2 - Lee, Minho
A2 - Hirose, Akira
A2 - Hou, Zeng-Guang
A2 - Kil, Rhee Man
PB - Springer
CY - Germany
ER -