We present a general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representation and enables a comparison between the text and images. In the experiments, we look at two approaches of retrieving images based on only their content from a text query. We compare orthogonalization approaches against a standard cross-representation retrieval technique known as the generalized vector space model.
Hardoon et al. REF Audio used kernel CCA (KCCA) for learning the semantic representation of images and their associated text.
Canonical Correlation Analysis: An Overview with Application to Learning Methods
Neural Computation
Neural Computation
Computer Science Medicine