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Cross-Modal Hashing With Feature Semi-Interaction and Semantic Ranking for Remote Sensing Ship Image Retrieval

Sun, Yuxi (författare)
Harbin Inst Technol, Dept Comp Sci, Shenzhen 518055, Peoples R China.
Ye, Yunming (författare)
Harbin Inst Technol, Dept Comp Sci, Shenzhen 518055, Peoples R China.
Kang, Jian (författare)
Soochow Univ, Sch Elect & Informat Engn, Suzhou 215006, Peoples R China.
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Fernandez-Beltran, Ruben (författare)
Univ Murcia, Dept Comp Sci & Syst, Murcia 30100, Spain.
Ban, Yifang (författare)
KTH,Geoinformatik
Hafner, Sebastian (författare)
KTH,Geoinformatik
Li, Xutao (författare)
Harbin Inst Technol, Dept Comp Sci, Shenzhen 518055, Peoples R China.
Luo, Chuyao (författare)
Harbin Inst Technol, Dept Comp Sci, Shenzhen 518055, Peoples R China.
Plaza, Antonio (författare)
Univ Extremadura, Escuela Politecn, Dept Technol Comp & Commun, Hyperspectral Comp Lab, Caceres 10003, Spain.
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Harbin Inst Technol, Dept Comp Sci, Shenzhen 518055, Peoples R China Soochow Univ, Sch Elect & Informat Engn, Suzhou 215006, Peoples R China. (creator_code:org_t)
Institute of Electrical and Electronics Engineers (IEEE), 2024
2024
Engelska.
Ingår i: IEEE Transactions on Geoscience and Remote Sensing. - : Institute of Electrical and Electronics Engineers (IEEE). - 0196-2892 .- 1558-0644. ; 62
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • Cross-modal hashing plays a pivotal role in large-scale remote sensing (RS) ship image retrieval. RS ship images often exhibit similar overall appearance with subtle differences. Existing hashing methods typically employ feature non-interaction strategies to generate common hash codes, which may not effectively capture the correlations between cross-modal ship images to reduce intermodality discrepancies. To address this issue, we propose a novel cross-modal hashing approach based on feature semi-interaction and semantic ranking (FSISR) for RS ship image retrieval. Our FSISR approach not only captures intricate correlations between different ship image modalities, but also enables the construction of hash tables for large-scale retrieval. FSISR comprises a feature semi-interaction module and a semantic ranking objective function. The semi-interaction module utilizes clustering centers from one modality to learn the correlations between two modalities and generate robust shared representations. The objective function optimizes these representations in a common Hamming space, consisting of a shared semantic alignment loss and a margin-free ranking loss. The alignment loss employs a shared semantic layer to preserve label-level similarity, while the ranking loss incorporates hard examples to establish a margin-free loss that captures similarity ranking relationships. We evaluate the performance of our method on benchmark datasets and demonstrate its effectiveness for cross-modal RS ship image retrieval. https://github.com/sunyuxi/FSISR.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)

Nyckelord

Marine vehicles
Semantics
Codes
Image retrieval
Correlation
Linear programming
Visualization
Cross-modal remote sensing (RS) ship images
deep supervised hashing
learning to hash
multisource RS images
RS ship image retrieval

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