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TDCMR: Triplet-Based Deep Cross-Modal Retrieval for geo-multimedia data
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Song, Jiagang, Lin, Yunwu, Song, Jiayu, Yu, Weiren and Zhang, Leyuan (2021) TDCMR: Triplet-Based Deep Cross-Modal Retrieval for geo-multimedia data. Applied Sciences, 11 (22). e10803. doi:10.3390/app112210803 ISSN 2076-3417.
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WRAP-TDCMR-Geo-multimedia-data-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1323Kb) | Preview |
Official URL: https://doi.org/10.3390/app112210803
Abstract
Mass multimedia data with geographical information (geo-multimedia) are collected and stored on the Internet due to the wide application of location-based services (LBS). How to find the high-level semantic relationship between geo-multimedia data and construct efficient index is crucial for large-scale geo-multimedia retrieval. To combat this challenge, the paper proposes a deep cross-modal hashing framework for geo-multimedia retrieval, termed as Triplet-based Deep Cross-Modal Retrieval (TDCMR), which utilizes deep neural network and an enhanced triplet constraint to capture high-level semantics. Besides, a novel hybrid index, called TH-Quadtree, is developed by combining cross-modal binary hash codes and quadtree to support high-performance search. Extensive experiments are conducted on three common used benchmarks, and the results show the superior performance of the proposed method.
| Item Type: | Journal Article | ||||||||||||||||||
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| Subjects: | G Geography. Anthropology. Recreation > G Geography (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||||||||
| SWORD Depositor: | Library Publications Router | ||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Multimedia systems, Mobile geographic information systems , Geodatabases, Geographic information systems , Location-based services , Semantic computing , Nearest neighbor analysis (Statistics) | ||||||||||||||||||
| Journal or Publication Title: | Applied Sciences | ||||||||||||||||||
| Publisher: | MDPI | ||||||||||||||||||
| ISSN: | 2076-3417 | ||||||||||||||||||
| Official Date: | 16 November 2021 | ||||||||||||||||||
| Dates: |
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| Volume: | 11 | ||||||||||||||||||
| Number: | 22 | ||||||||||||||||||
| Article Number: | e10803 | ||||||||||||||||||
| DOI: | 10.3390/app112210803 | ||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||||||||
| Date of first compliant deposit: | 30 November 2021 | ||||||||||||||||||
| Date of first compliant Open Access: | 30 November 2021 | ||||||||||||||||||
| RIOXX Funder/Project Grant: |
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