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Related Experiment Videos

Canonical correlation analysis: an overview with application to learning methods.

David R Hardoon1, Sandor Szedmak, John Shawe-Taylor

  • 1School of Electronics and Computer Science, Image, Speech and Intelligent Systems Research Group, University of Southampton, Southampton S017 1BJ, UK. drh@ecs.soton.ac.uk

Neural Computation
|November 2, 2004
PubMed
Summary
This summary is machine-generated.

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We developed a kernel canonical correlation analysis method to create a shared semantic space for web images and text. This enables effective image retrieval using text queries, comparing new methods against the generalized vector space model.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Web image and text data are often multimodal and lack a unified representation.
  • Effective cross-modal retrieval remains a challenge in information retrieval.

Purpose of the Study:

  • To introduce a general method for learning a joint semantic representation of web images and associated text.
  • To enable effective content-based image retrieval using text queries.

Main Methods:

  • Kernel Canonical Correlation Analysis (KCCA) was employed to learn a shared semantic space.
  • Two image retrieval approaches were investigated: orthogonalization and a generalized vector space model.

Main Results:

  • The learned semantic space allows for direct comparison between textual and visual data.

Related Experiment Videos

  • Experimental results demonstrate the efficacy of the proposed method for cross-modal retrieval.

    • Conclusions:

    • Kernel Canonical Correlation Analysis provides a robust framework for multimodal semantic learning.
    • The developed method offers a viable alternative to traditional cross-representation retrieval techniques for image search.