A TV-logo classification and learning system

Logotypes superimposed to broadcasted videos supply important information for semantic video annotation, such as the content creator. In this work a novel logo classification and learning system for TV broadcast videos is presented. Logos are segmented from the video stream but scale change, position shift, clutter and noise makes difficult to classify and to recognize them. Several robust features that use edges and shape information have been selected, and a Bayesian network classifier is used to classify the logos. New logos are recognized as such for the first time they appear and passed to a semi-supervised learning system. The learning process clusters the set of new logos to group different instances of the same new logo. A logo model is obtained for each cluster that must be validated by a human to incorporate them into the classification system. Comprehensive tests with a set of 724 TV logos show the high performance of our classification and learning system.