Towards large scale cross-media retrieval via modeling heterogeneous information and exploring an efficient indexing scheme

Authors:
Bo Lu;Guoren Wang;Ye Yuan
Affiliations:
Key Laboratory of Medical Image Computing, Ministry of Education, School of Information Science & Engineering, Northeastern University, China;Key Laboratory of Medical Image Computing, Ministry of Education, School of Information Science & Engineering, Northeastern University, China;Key Laboratory of Medical Image Computing, Ministry of Education, School of Information Science & Engineering, Northeastern University, China
Venue:
CVM'12 Proceedings of the First international conference on Computational Visual Media
Year:
2012

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Abstract

With the rapid development of Internet and multimedia technology, cross-media retrieval is concerned to retrieve all the related media objects with multi-modality by submitting a query media object. In this paper, we propose a novel method which is dedicate to achieve effective and accurate cross-media retrieval. Firstly, a Multi-modality Semantic Relationship Graph (MSRG) is constructed by using the semantic correlation amongst the media objects with multi-modality. Secondly, all the media objects in MSRG are mapped onto an isomorphic semantic space. Further, an efficient indexing MK-tree based on heterogeneous data distribution is proposed to manage the media objects within the semantic space and improve the performance of cross-media retrieval. Extensive experiments on real large scale cross-media datasets indicate that our proposal dramatically improves the accuracy and efficiency of cross-media retrieval, outperforming the existing methods significantly.