KCCA for different level precision in content-based image retrieval

We use kernel Canonical Correlation Analysis to learn a semantic representation of web images and their associated text. In the application we look at two approaches of retrieving images based only on their content from a text query. The semantic space provides a common representation and enables a comparison between the text and image. We compare the approaches against a standard cross-representation retrieval technique known as the Generalised Vector Space Model.