IEICE TRANSACTIONS on Information
Global Selection vs Local Ordering of Color SIFT Independent Components for Object/Scene Classification
Summary :
This paper addresses the problem of ordering the color SIFT descriptors in the independent component analysis for image classification. Component ordering is of great importance for image classification, since it is the foundation of feature selection. To select distinctive and compact independent components (IC) of the color SIFT descriptors, we propose two ordering approaches based on local variation, named as the localization-based IC ordering and the sparseness-based IC ordering. We evaluate the performance of proposed methods, the conventional IC selection method (global variation based components selection) and original color SIFT descriptors on object and scene databases, and obtain the following two main results. First, the proposed methods are able to obtain acceptable classification results in comparison with original color SIFT descriptors. Second, the highest classification rate can be obtained by using the global selection method in the scene database, while the local ordering methods give the best performance for the object database.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.9 pp.1800-1808
- Publication Date
- 2011/09/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.1800
- Type of Manuscript
- PAPER
- Category
- Pattern Recognition
Authors
Keyword
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Dan-ni AI, Xian-hua HAN, Guifang DUAN, Xiang RUAN, Yen-wei CHEN, "Global Selection vs Local Ordering of Color SIFT Independent Components for Object/Scene Classification" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 9, pp. 1800-1808, September 2011, doi: 10.1587/transinf.E94.D.1800.
Abstract: This paper addresses the problem of ordering the color SIFT descriptors in the independent component analysis for image classification. Component ordering is of great importance for image classification, since it is the foundation of feature selection. To select distinctive and compact independent components (IC) of the color SIFT descriptors, we propose two ordering approaches based on local variation, named as the localization-based IC ordering and the sparseness-based IC ordering. We evaluate the performance of proposed methods, the conventional IC selection method (global variation based components selection) and original color SIFT descriptors on object and scene databases, and obtain the following two main results. First, the proposed methods are able to obtain acceptable classification results in comparison with original color SIFT descriptors. Second, the highest classification rate can be obtained by using the global selection method in the scene database, while the local ordering methods give the best performance for the object database.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.1800/_p
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@ARTICLE{e94-d_9_1800,
author={Dan-ni AI, Xian-hua HAN, Guifang DUAN, Xiang RUAN, Yen-wei CHEN, },
journal={IEICE TRANSACTIONS on Information},
title={Global Selection vs Local Ordering of Color SIFT Independent Components for Object/Scene Classification},
year={2011},
volume={E94-D},
number={9},
pages={1800-1808},
abstract={This paper addresses the problem of ordering the color SIFT descriptors in the independent component analysis for image classification. Component ordering is of great importance for image classification, since it is the foundation of feature selection. To select distinctive and compact independent components (IC) of the color SIFT descriptors, we propose two ordering approaches based on local variation, named as the localization-based IC ordering and the sparseness-based IC ordering. We evaluate the performance of proposed methods, the conventional IC selection method (global variation based components selection) and original color SIFT descriptors on object and scene databases, and obtain the following two main results. First, the proposed methods are able to obtain acceptable classification results in comparison with original color SIFT descriptors. Second, the highest classification rate can be obtained by using the global selection method in the scene database, while the local ordering methods give the best performance for the object database.},
keywords={},
doi={10.1587/transinf.E94.D.1800},
ISSN={1745-1361},
month={September},}
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TY - JOUR
TI - Global Selection vs Local Ordering of Color SIFT Independent Components for Object/Scene Classification
T2 - IEICE TRANSACTIONS on Information
SP - 1800
EP - 1808
AU - Dan-ni AI
AU - Xian-hua HAN
AU - Guifang DUAN
AU - Xiang RUAN
AU - Yen-wei CHEN
PY - 2011
DO - 10.1587/transinf.E94.D.1800
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2011
AB - This paper addresses the problem of ordering the color SIFT descriptors in the independent component analysis for image classification. Component ordering is of great importance for image classification, since it is the foundation of feature selection. To select distinctive and compact independent components (IC) of the color SIFT descriptors, we propose two ordering approaches based on local variation, named as the localization-based IC ordering and the sparseness-based IC ordering. We evaluate the performance of proposed methods, the conventional IC selection method (global variation based components selection) and original color SIFT descriptors on object and scene databases, and obtain the following two main results. First, the proposed methods are able to obtain acceptable classification results in comparison with original color SIFT descriptors. Second, the highest classification rate can be obtained by using the global selection method in the scene database, while the local ordering methods give the best performance for the object database.
ER -
English
