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 adopted KCCA to retrieve images based on their content using the text query.
Canonical Correlation Analysis: An Overview with Application to Learning Methods
Neural Computation
Neural Computation
Computer Science Medicine