Currently, image retrieval by content is a research problem of great interest in academia and the industry, due to the large collections of images available in different contexts. One of the main challenges to develop effective image retrieval systems is the automatic identification of semantic image contents. This research proposal aims to design a model for semantic image retrieval able to take advantage of different data sources, i.e. using multimodal information, to improve the response of an image retrieval system. In particular two data modalities associated to contents and context of images are considered in this proposal: visual features and unstructured text annotations. The proposed framework is based on kernel methods that provide two main important advantages over the traditional multimodal approaches: first, the structure of each modality is preserved in a high dimensional feature space, and second, they provide natural ways to fuse feature spaces in a unique information space. This document presents the research agenda to build a Multimodal Information Space for searching images by content.