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Towards Large Scale Cross-Media Retrieval via Modeling Heterogeneous Information and Exploring an Efficient Indexing Scheme

  • Bo Lu
  • Guoren Wang
  • Ye Yuan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7633)

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.

Keywords

Query Processing Canonical Correlation Analysis Average Precision Range Query Semantic Concept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bo Lu
    • 1
  • Guoren Wang
    • 1
  • Ye Yuan
    • 1
  1. 1.Key Laboratory of Medical Image Computing, Ministry of Education, School of Information Science & EngineeringNortheastern UniversityChina

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