Handling imperfections for multimodal image annotation

This thesis deals with multimodal image annotation in the context of social media. We seek to take advantage of textual (tags) and visual information in order to enhance the image annotation performances. However, these tags are often noisy, overly personalized and only a few of them are related to the semantic visual content of the image. In addition, when combining prediction scores from different classifiers learned on different modalities, multimodal image annotation faces their imperfections (uncertainty, imprecision and incompleteness). Consequently, we consider that multimodal image annotation is subject to imperfections at two levels: the representation and the decision. Inspired from the information fusion theory, we focus in this thesis on defining, identifying and handling imperfection aspects in order to improve image annotation.