Relatively-Paired Space Analysis
Zhanghui Kuang, Kenneth K. Wong · 2013
Discovering a latent common space between different modalities plays an important role in cross-modality pattern recognition. Existing techniques often require absolutely-paired observations as training data, and are incapable of capturing more general seman-tic relationships between cross-modality observations. This greatly limits their appli-cations. In this paper, we propose a general framework for learning a latent common space from relatively-paired observations (i.e., two observations from different modali-ties 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 out-performs other state-of-the-art approaches. 1