A Graph Model for Cross-modal Retrieval

DOI

10.2991/icmt-13.2013.133How to use a DOI?

Keywords

Cross-modal retrieval • Graph model • Content similarity • Semantics similarity • Interaction

Abstract

With the rapid growth of multimedia document on the web, cross-modal retrieval has become an important issue. The modality of a query is different from that of the retrieved results in cross-modal retrieval. In this paper, we propose a novel graph model, which not only combines content and semantics similarities through two Markov chains, but also utilizes the interaction between different modalities to attain the whole semantics information of a multimedia document. Content similarity focuses on the original features within each modality, while semantics similarity focuses on the semantic vectors in a common space. Both of them are very significant. Random forests method is used to map the original features into a semantic space. The ranked list for a query is achieved by highlighting an optimal path across the corresponding chain. Experiments on the Wikipedia dataset show that the performance of our model significantly outperforms those of existing approaches for cross-modal retrieval.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Wang Shixun
AU  - Pan Peng
AU  - Lu Yansheng
PY  - 2013/11
DA  - 2013/11
TI  - A Graph Model for Cross-modal Retrieval
BT  - Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)
PB  - Atlantis Press
SP  - 1083
EP  - 1090
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmt-13.2013.133
DO  - 10.2991/icmt-13.2013.133
ID  - Shixun2013/11
ER  -