1 research outputs found
An Efficient Approach for Geo-Multimedia Cross-Modal Retrieval
Due to the rapid development of mobile Internet techniques, cloud computation
and popularity of 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
search out a set of geo-multimedia objects based on geographical distance
proximity and semantic similarity between different modalities. 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 and do not focus on this type of query. In order to address this
problem efficiently, we present the definition of NN geo-multimedia
cross-modal query at the first time and introduce relevant conceptions such as
cross-modal semantic representation space. To bridge the semantic gap between
different modalities, we propose a method named cross-modal semantic matching
which contains two important component, i.e., CorrProj and LogsTran, which aims
to construct a common semantic representation space for cross-modal semantic
similarity measurement. Besides, we designed a framework based on deep learning
techniques to implement common semantic representation space construction. In
addition, a novel hybrid indexing structure named GMR-Tree combining
geo-multimedia data and R-Tree is presented and a efficient NN search
algorithm called GMCMS is designed. Comprehensive experimental evaluation on
real and synthetic dataset clearly demonstrates that our solution outperforms
the-state-of-the-art methods.Comment: 27 page