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Boosting Multimodal Semantic Understanding by Local Similarity Adaptation and Global Correlation Propagation

  • Hong Zhang
  • Xiaoli Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)

Abstract

An important trend in multimedia semantic understanding is the utilization and support of multimodal data which are heterogeneous in low-level features, such as image and audio. The main challenge is how to measure different kinds of correlations among multimodal data. In this paper, we propose a novel approach to boost multimodal semantic understanding from local and global perspectives. First, cross-media correlation between images and audio clips is estimated with Kernel Canonical Correlation Analysis; secondly, a multimodal graph is constructed to enable global correlation propagation with adapted intra-media similarity; then cross-media retrieval algorithm is discussed as an application of our approach. A prototype system is developed to demonstrate the feasibility and capability. Experimental results are encouraging and show that the performance of our approach is effective.

Keywords

multimodal semantics correlation propagation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Hong Zhang
    • 1
  • Xiaoli Liu
    • 1
  1. 1.College of Computer Science & TechnologyWuhan University of Science & TechnologyWuhan

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