  Automatic image annotation is among the fundamental problems in computer
vision and pattern recognition, and it is becoming increasingly important in
order to develop algorithms that are able to search and browse large-scale
image collections. In this paper, we propose a label propagation framework
based on Kernel Canonical Correlation Analysis (KCCA), which builds a latent
semantic space where correlation of visual and textual features are well
preserved into a semantic embedding. The proposed approach is robust and can
work either when the training set is well annotated by experts, as well as when
it is noisy such as in the case of user-generated tags in social media. We
report extensive results on four popular datasets. Our results show that our
KCCA-based framework can be applied to several state-of-the-art label transfer
methods to obtain significant improvements. Our approach works even with the
noisy tags of social users, provided that appropriate denoising is performed.
Experiments on a large scale setting show that our method can provide some
benefits even when the semantic space is estimated on a subset of training
images.

Computer Vision and Pattern Recognition;Information Retrieval;Multimedia