Semantic Link Network-Based Model for Organizing Multimedia Big Data

Recent research shows that multimedia resources in the wild are growing at a staggering rate. The rapid increase number of multimedia resources has brought an urgent need to develop intelligent methods to organize and process them. In this paper, the semantic link network model is used for organizin...

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Bibliographic Details
Published in:IEEE transactions on emerging topics in computing 2014-09, Vol.2 (3), p.376-387
Main Authors: Hu, Chuanping, Xu, Zheng, Liu, Yunhuai, Mei, Lin, Chen, Lan, Luo, Xiangfeng
Format: Article
Language:English
Subjects:
Big data
Computational modeling
Data models
Multimedia communication
Ontologies
Semantics
Streaming media
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Summary:Recent research shows that multimedia resources in the wild are growing at a staggering rate. The rapid increase number of multimedia resources has brought an urgent need to develop intelligent methods to organize and process them. In this paper, the semantic link network model is used for organizing multimedia resources. A whole model for generating the association relation between multimedia resources using semantic link network model is proposed. The definitions, modules, and mechanisms of the semantic link network are used in the proposed method. The integration between the semantic link network and multimedia resources provides a new prospect for organizing them with their semantics. The tags and the surrounding texts of multimedia resources are used to measure their semantic association. The hierarchical semantic of multimedia resources is defined by their annotated tags and surrounding texts. The semantics of tags and surrounding texts are different in the proposed framework. The modules of semantic link network model are implemented to measure association relations. A real data set including 100 thousand images with social tags from Flickr is used in our experiments. Two evaluation methods, including clustering and retrieval, are performed, which shows the proposed method can measure the semantic relatedness between Flickr images accurately and robustly.
ISSN:2168-6750
2168-6750
DOI:10.1109/TETC.2014.2316525