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RISS 인기검색어
- 검색키워드
- 저자 : Zhenhong Du (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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An Object-Level Feature Representation Model for the Multi-target Retrieval of Remote Sensing Images
Zeng, Zhi,Du, Zhenhong,Liu, Renyi Korean Institute of Information Scientists and Eng 2014 Journal of Computing Science and Engineering Vol.8 No.2
To address the problem of multi-target retrieval (MTR) of remote sensing images, this study proposes a new object-level feature representation model. The model provides an enhanced application image representation that improves the efficiency of MTR. Generating the model in our scheme includes processes, such as object-oriented image segmentation, feature parameter calculation, and symbolic image database construction. The proposed model uses the spatial representation method of the extended nine-direction lower-triangular (9DLT) matrix to combine spatial relationships among objects, and organizes the image features according to MPEG-7 standards. A similarity metric method is proposed that improves the precision of similarity retrieval. Our method provides a trade-off strategy that supports flexible matching on the target features, or the spatial relationship between the query target and the image database. We implement this retrieval framework on a dataset of remote sensing images. Experimental results show that the proposed model achieves competitive and high-retrieval precision.
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An Object-Level Feature Representation Model for the Multi-target Retrieval of Remote Sensing Images
Zhi Zeng,Zhenhong Du,Renyi Liu 한국정보과학회 2014 Journal of Computing Science and Engineering Vol.8 No.2
To address the problem of multi-target retrieval (MTR) of remote sensing images, this study proposes a new object-level feature representation model. The model provides an enhanced application image representation that improves the efficiency of MTR. Generating the model in our scheme includes processes, such as object-oriented image segmentation, feature parameter calculation, and symbolic image database construction. The proposed model uses the spatial representation method of the extended nine-direction lower-triangular (9DLT) matrix to combine spatial relationships among objects, and organizes the image features according to MPEG-7 standards. A similarity metric method is proposed that improves the precision of similarity retrieval. Our method provides a trade-off strategy that supports flexible matching on the target features, or the spatial relationship between the query target and the image database. We implement this retrieval framework on a dataset of remote sensing images. Experimental results show that the proposed model achieves competitive and high-retrieval precision.
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