Relatively-Paired Space Analysis

author: Zhanghui Kuang, Department of Computer Science, University of Hong Kong
published: April 3, 2014,   recorded: September 2013,   views: 2110
Categories

Slides

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

Discovering a latent common space between different modalities plays an important role in cross-modality pattern recognition. Existing techniques often require absolutelypaired observations as training data, and are incapable of capturing more general semantic relationships between cross-modality observations. This greatly limits their applications. In this paper, we propose a general framework for learning a latent common space from relatively-paired observations (i.e., two observations from different modalities are more-likely-paired than another two). Relative-pairing information is encoded using relative proximities of observations in the latent common space. By building a discriminative model and maximizing a distance margin, a projection function that maps observations into the latent common space is learned for each modality. Cross-modality pattern recognition can then be carried out in the latent common space. To evaluate its performance, the proposed framework has been applied to cross-pose face recognition and feature fusion. Experimental results demonstrate that the proposed framework outperforms other state-of-the-art approaches.

See Also:

Download slides icon Download slides: bmvc2013_kuang_space_analysis_01.pdf (643.6┬áKB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: