An Efficient Approach for Geo-Multimedia Cross-Modal Retrieval

Lei Zhu, Jun Long, Chengyuan Zhang, Weiren Yu, Xinpan Yuan, Longzhi Sun

Research output: Contribution to journalArticlepeer-review


Due to the rapid development of mobile Internet techniques, such as online social networking and location-based services, massive amount of multimedia data with geographical information is generated and uploaded to the Internet. In this paper, we propose a novel type of cross-modal multimedia retrieval, called geo-multimedia cross-modal retrieval, which aims to find a set of geo-multimedia objects according to geographical distance proximity and semantic concept similarity. Previous studies for cross-modal retrieval and spatial keyword search cannot address this problem effectively because they do not consider multimedia data with geo-tags (geo-multimedia). Firstly, we present the definition of k NN geo-multimedia cross-modal query and introduce relevant concepts such as spatial distance and semantic similarity measurement. As the key notion of this work, cross-modal semantic representation space is formulated at the first time. A novel framework for geo-multimedia cross-modal retrieval is proposed, which includes multi-modal feature extraction, cross-modal semantic space mapping, geo-multimedia spatial index and cross-modal semantic similarity measurement. To bridge the semantic gap between different modalities, we also propose a method named cross-modal semantic matching (CoSMat for shot) which contains two important components, i.e., CorrProj and LogsTran, which aims to build a common semantic representation space for cross-modal semantic similarity measurement. In addition, to implement semantic similarity measurement, we employ deep learning based method to learn multi-modal features that contains more high level semantic information. Moreover, a novel hybrid index, GMR-Tree is carefully designed, which combines signatures of semantic representations and R-Tree. An efficient GMR-Tree based k NN search algorithm called k GMCMS is developed. Comprehensive experimental evaluations on real and synthetic datasets clearly demonstrate that our approach outperforms the-state-of-the-art methods.

Original languageEnglish
Article number8827517
Pages (from-to)180571-180589
Number of pages19
JournalIEEE Access
Early online date9 Sept 2019
Publication statusPublished - 23 Dec 2019

Bibliographical note

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  • Cross-modal retrieval
  • deep learning
  • geo-multimedia
  • kNN spatial search


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