Efficient Wavelet-Based Image Retrieval Using Coarse Segmentation and Fine Region Feature Extraction
Summary :
Semantic image segmentation and appropriate region content description are crucial issues for region-based image retrieval (RBIR). In this paper, a novel region-based image retrieval method is proposed, which performs fast coarse image segmentation and fine region feature extraction using the decomposition property of image wavelet transform. First, coarse image segmentation is conducted efficiently in the Low-Low(LL) frequency subband of image wavelet transform. Second, the feature vector of each segmented region is hierarchically extracted from all different wavelet frequency subbands, which captures the distinctive feature (e.g., semantic texture) inside one region finely. Experiment results show the efficiency and the effectiveness of the proposed method for region-based image retrieval.
- Publication
- IEICE TRANSACTIONS on Information Vol.E88-D No.5 pp.1021-1030
- Publication Date
- 2005/05/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietisy/e88-d.5.1021
- Type of Manuscript
- PAPER
- Category
- Image Processing and Video Processing
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Yongqing SUN, Shinji OZAWA, "Efficient Wavelet-Based Image Retrieval Using Coarse Segmentation and Fine Region Feature Extraction" in IEICE TRANSACTIONS on Information,
vol. E88-D, no. 5, pp. 1021-1030, May 2005, doi: 10.1093/ietisy/e88-d.5.1021.
Abstract: Semantic image segmentation and appropriate region content description are crucial issues for region-based image retrieval (RBIR). In this paper, a novel region-based image retrieval method is proposed, which performs fast coarse image segmentation and fine region feature extraction using the decomposition property of image wavelet transform. First, coarse image segmentation is conducted efficiently in the Low-Low(LL) frequency subband of image wavelet transform. Second, the feature vector of each segmented region is hierarchically extracted from all different wavelet frequency subbands, which captures the distinctive feature (e.g., semantic texture) inside one region finely. Experiment results show the efficiency and the effectiveness of the proposed method for region-based image retrieval.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e88-d.5.1021/_p
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@ARTICLE{e88-d_5_1021,
author={Yongqing SUN, Shinji OZAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Efficient Wavelet-Based Image Retrieval Using Coarse Segmentation and Fine Region Feature Extraction},
year={2005},
volume={E88-D},
number={5},
pages={1021-1030},
abstract={Semantic image segmentation and appropriate region content description are crucial issues for region-based image retrieval (RBIR). In this paper, a novel region-based image retrieval method is proposed, which performs fast coarse image segmentation and fine region feature extraction using the decomposition property of image wavelet transform. First, coarse image segmentation is conducted efficiently in the Low-Low(LL) frequency subband of image wavelet transform. Second, the feature vector of each segmented region is hierarchically extracted from all different wavelet frequency subbands, which captures the distinctive feature (e.g., semantic texture) inside one region finely. Experiment results show the efficiency and the effectiveness of the proposed method for region-based image retrieval.},
keywords={},
doi={10.1093/ietisy/e88-d.5.1021},
ISSN={},
month={May},}
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TY - JOUR
TI - Efficient Wavelet-Based Image Retrieval Using Coarse Segmentation and Fine Region Feature Extraction
T2 - IEICE TRANSACTIONS on Information
SP - 1021
EP - 1030
AU - Yongqing SUN
AU - Shinji OZAWA
PY - 2005
DO - 10.1093/ietisy/e88-d.5.1021
JO - IEICE TRANSACTIONS on Information
SN -
VL - E88-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2005
AB - Semantic image segmentation and appropriate region content description are crucial issues for region-based image retrieval (RBIR). In this paper, a novel region-based image retrieval method is proposed, which performs fast coarse image segmentation and fine region feature extraction using the decomposition property of image wavelet transform. First, coarse image segmentation is conducted efficiently in the Low-Low(LL) frequency subband of image wavelet transform. Second, the feature vector of each segmented region is hierarchically extracted from all different wavelet frequency subbands, which captures the distinctive feature (e.g., semantic texture) inside one region finely. Experiment results show the efficiency and the effectiveness of the proposed method for region-based image retrieval.
ER -
English
