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. © 2012 Springer-Verlag.
CITATION STYLE
Lu, B., Wang, G., & Yuan, Y. (2012). Towards large scale cross-media retrieval via modeling heterogeneous information and exploring an efficient indexing scheme. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7633 LNCS, pp. 202–209). https://doi.org/10.1007/978-3-642-34263-9_26
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