IEICE TRANSACTIONS on Information
Color Independent Components Based SIFT Descriptors for Object/Scene Classification
Summary :
In this paper, we present a novel color independent components based SIFT descriptor (termed CIC-SIFT) for object/scene classification. We first learn an efficient color transformation matrix based on independent component analysis (ICA), which is adaptive to each category in a database. The ICA-based color transformation can enhance contrast between the objects and the background in an image. Then we compute CIC-SIFT descriptors over all three transformed color independent components. Since the ICA-based color transformation can boost the objects and suppress the background, the proposed CIC-SIFT can extract more effective and discriminative local features for object/scene classification. The comparison is performed among seven SIFT descriptors, and the experimental classification results show that our proposed CIC-SIFT is superior to other conventional SIFT descriptors.
- Publication
- IEICE TRANSACTIONS on Information Vol.E93-D No.9 pp.2577-2586
- Publication Date
- 2010/09/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E93.D.2577
- Type of Manuscript
- PAPER
- Category
- Pattern Recognition
Authors
Keyword
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Dan-ni AI, Xian-hua HAN, Xiang RUAN, Yen-wei CHEN, "Color Independent Components Based SIFT Descriptors for Object/Scene Classification" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 9, pp. 2577-2586, September 2010, doi: 10.1587/transinf.E93.D.2577.
Abstract: In this paper, we present a novel color independent components based SIFT descriptor (termed CIC-SIFT) for object/scene classification. We first learn an efficient color transformation matrix based on independent component analysis (ICA), which is adaptive to each category in a database. The ICA-based color transformation can enhance contrast between the objects and the background in an image. Then we compute CIC-SIFT descriptors over all three transformed color independent components. Since the ICA-based color transformation can boost the objects and suppress the background, the proposed CIC-SIFT can extract more effective and discriminative local features for object/scene classification. The comparison is performed among seven SIFT descriptors, and the experimental classification results show that our proposed CIC-SIFT is superior to other conventional SIFT descriptors.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2577/_p
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@ARTICLE{e93-d_9_2577,
author={Dan-ni AI, Xian-hua HAN, Xiang RUAN, Yen-wei CHEN, },
journal={IEICE TRANSACTIONS on Information},
title={Color Independent Components Based SIFT Descriptors for Object/Scene Classification},
year={2010},
volume={E93-D},
number={9},
pages={2577-2586},
abstract={In this paper, we present a novel color independent components based SIFT descriptor (termed CIC-SIFT) for object/scene classification. We first learn an efficient color transformation matrix based on independent component analysis (ICA), which is adaptive to each category in a database. The ICA-based color transformation can enhance contrast between the objects and the background in an image. Then we compute CIC-SIFT descriptors over all three transformed color independent components. Since the ICA-based color transformation can boost the objects and suppress the background, the proposed CIC-SIFT can extract more effective and discriminative local features for object/scene classification. The comparison is performed among seven SIFT descriptors, and the experimental classification results show that our proposed CIC-SIFT is superior to other conventional SIFT descriptors.},
keywords={},
doi={10.1587/transinf.E93.D.2577},
ISSN={1745-1361},
month={September},}
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TY - JOUR
TI - Color Independent Components Based SIFT Descriptors for Object/Scene Classification
T2 - IEICE TRANSACTIONS on Information
SP - 2577
EP - 2586
AU - Dan-ni AI
AU - Xian-hua HAN
AU - Xiang RUAN
AU - Yen-wei CHEN
PY - 2010
DO - 10.1587/transinf.E93.D.2577
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2010
AB - In this paper, we present a novel color independent components based SIFT descriptor (termed CIC-SIFT) for object/scene classification. We first learn an efficient color transformation matrix based on independent component analysis (ICA), which is adaptive to each category in a database. The ICA-based color transformation can enhance contrast between the objects and the background in an image. Then we compute CIC-SIFT descriptors over all three transformed color independent components. Since the ICA-based color transformation can boost the objects and suppress the background, the proposed CIC-SIFT can extract more effective and discriminative local features for object/scene classification. The comparison is performed among seven SIFT descriptors, and the experimental classification results show that our proposed CIC-SIFT is superior to other conventional SIFT descriptors.
ER -
English
