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We present a novel approach to automatically annotate images using associated text. We detect and classify all entities (persons and objects) in the text after which we determine the salience (the importance of an entity in a text) and visualness (the extent to which an entity can be perceived visually) of these entities. We combine these measures to compute the probability that an entity is present in the image. The suitability of our approach was successfully tested on 100 image-text pairs of Yahoo! News. . | Text Analysis for Automatic Image Annotation Koen Deschacht and Marie-Francine Moens Interdisciplinary Centre for Law IT Department of Computer Science Katholieke Universiteit Leuven Tiensestraat 41 3000 Leuven Belgium koen.deschacht marie-france.moens @law.kuleuven.ac.be Abstract We present a novel approach to automatically annotate images using associated text. We detect and classify all entities persons and objects in the text after which we determine the salience the importance of an entity in a text and visualness the extent to which an entity can be perceived visually of these entities. We combine these measures to compute the probability that an entity is present in the image. The suitability of our approach was successfully tested on 100 image-text pairs of Yahoo News. 1 Introduction Our society deals with a growing bulk of unstructured information such as text images and video a situation witnessed in many domains news biomedical information intelligence information business documents etc. . This growth comes along with the demand for more effective tools to search and summarize this information. Moreover there is the need to mine information from texts and images when they contribute to decision making by governments businesses and other institutions. The capability to accurately recognize content in these sources would largely contribute to improved indexing classification filtering mining and interrogation. Algorithms and techniques for the disclosure of information from the different media have been developed for every medium independently during the last decennium but only recently the interplay between these different media has become a topic of 1000 interest. One of the possible applications is to help analysis in one medium by employing information from another medium. In this paper we study text that is associated with an image such as for instance image captions video transcripts or surrounding text in a web page. We develop techniques that .
