Image-Language Association: are we looking at the right features?

The ever growing popularity and availability of multimedia information has rendered automatic image-language association essential in a number of multimedia integration applications. Bridging the gap between the two media requires an appropriate feature-set for describing their common reference; one that will be both distinctive of the entities referred too and feasible to extract automatically from visual media. In this presentation, we suggest an alternative –to current approaches- feature set, which has been used in OntoVis, a domain model for a prototype that describes three-dimensional (3D) indoor scenes. We argue that it is worth employing this feature-set in a larger scale for image-language association and investigating the feasibility of doing so and of detecting such features automatically even beyond 3D visual data, in 2D images.

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