IEICE TRANSACTIONS on Information
Natural Object/Artifact Image Classification Based on Line Features
Summary :
Three features for image classification into natural objects and artifacts are investigated. They are 'line length ratio', 'line direction distribution,' and 'edge coverage'. Among the three, the feature 'line length ratio' shows superior classification accuracy (above 90%) that exceeds the performance of conventional features, according to experimental results in application to digital camera images. As the development of this feature was motivated by the fact that the edge sharpening magnitude in image-quality improvement must be controlled based on the image content, this classification algorithm should be especially suitable for the image-quality improvement applications.
- Publication
- IEICE TRANSACTIONS on Information Vol.E91-D No.8 pp.2207-2211
- Publication Date
- 2008/08/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e91-d.8.2207
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Johji TAJIMA, Hironori KONO, "Natural Object/Artifact Image Classification Based on Line Features" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 8, pp. 2207-2211, August 2008, doi: 10.1093/ietisy/e91-d.8.2207.
Abstract: Three features for image classification into natural objects and artifacts are investigated. They are 'line length ratio', 'line direction distribution,' and 'edge coverage'. Among the three, the feature 'line length ratio' shows superior classification accuracy (above 90%) that exceeds the performance of conventional features, according to experimental results in application to digital camera images. As the development of this feature was motivated by the fact that the edge sharpening magnitude in image-quality improvement must be controlled based on the image content, this classification algorithm should be especially suitable for the image-quality improvement applications.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.8.2207/_p
Copy
@ARTICLE{e91-d_8_2207,
author={Johji TAJIMA, Hironori KONO, },
journal={IEICE TRANSACTIONS on Information},
title={Natural Object/Artifact Image Classification Based on Line Features},
year={2008},
volume={E91-D},
number={8},
pages={2207-2211},
abstract={Three features for image classification into natural objects and artifacts are investigated. They are 'line length ratio', 'line direction distribution,' and 'edge coverage'. Among the three, the feature 'line length ratio' shows superior classification accuracy (above 90%) that exceeds the performance of conventional features, according to experimental results in application to digital camera images. As the development of this feature was motivated by the fact that the edge sharpening magnitude in image-quality improvement must be controlled based on the image content, this classification algorithm should be especially suitable for the image-quality improvement applications.},
keywords={},
doi={10.1093/ietisy/e91-d.8.2207},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Natural Object/Artifact Image Classification Based on Line Features
T2 - IEICE TRANSACTIONS on Information
SP - 2207
EP - 2211
AU - Johji TAJIMA
AU - Hironori KONO
PY - 2008
DO - 10.1093/ietisy/e91-d.8.2207
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2008
AB - Three features for image classification into natural objects and artifacts are investigated. They are 'line length ratio', 'line direction distribution,' and 'edge coverage'. Among the three, the feature 'line length ratio' shows superior classification accuracy (above 90%) that exceeds the performance of conventional features, according to experimental results in application to digital camera images. As the development of this feature was motivated by the fact that the edge sharpening magnitude in image-quality improvement must be controlled based on the image content, this classification algorithm should be especially suitable for the image-quality improvement applications.
ER -
English
