This thesis describes the graphical user interface developed for semi-automatic keyframebased semantic shot annotation and the semantic shot classifiers built. The graphical user interface aims to optimize the current indexation process by substituting manual annotation for automatic annotation and validation. The system is based on supervised learning binary classifiers and web services. The graphical user interface provides the necessary tools to fix and validate the automatic detections and to learn from the user feedback to retrain the system and improve it. Results of the classifiers evaluation, performed using cross-validation methods, show a good performance in terms of precision and recall. The graphical user interface has been described as complete and easy to use by a professional documentalist at a broadcast company.