Exploiting Context Information for Image Description

Integrating ontological knowledge is a promising research direction to improve automatic image description. In particular, when probabilistic ontologies are available, the corresponding probabilities could be combined with the probabilities produced by a multi-class classifier applied to different parts in an image. This combination not only provides the relations existing between the different segments, but can also improve the classification accuracy. In fact, the context often gives cues suggesting the correct class of the segment. This paper discusses a possible implementation of this integration, and the first experimental results shows its effectiveness when the classifier accuracy is relatively low. For the assessment of the performance we constructed a simulated classifier which allows the a priori decision of its performance with a sufficient precision.