## 5 Experiments
### Discussion  
In the experiments section of this work, we wanted to demonstrate that the proposed framework facilitates the annotation and retrieval process of video portals significantly. Thus, we developed a freely available annotation prototype [102] and performed an initial case study on it. This case study shows that at least interactive object annotation can successfully be used to annotate a few objects in several scenes of a video in order to enable object-based and scene-based video retrieval. The annotation time that a user has to spend in this interactive process was measured with 1.5 min for short videos and with less than 8 min for entire feature films. We believe that many video portal users are willing to spend such an effort if their videos gain higher visibility as a revenue. Furthermore, we showed that the processing time and storage consumption of all processing steps is moderate and we depicted several opportunities for the framework's integration in video portals that allows analysis on the client as well as on the server. The results of the TRECVID challenge support the framework's usability for video portals as a similar number of correct object instances were detected under the topmost shots although a much larger dataset was used. The challenge further shows that the proposed framework is capable of producing competitive video object recognition results. The achieved results are above the median of all 14 participating teams though they are not the best overall results. However, we do not intend to prove that the proposed framework would work better than all other systems because further recognition approaches (including the ones that performed best in the challenge) can easily be integrated in the framework. Moreover, example-based video object retrieval is not the focus of the proposed framework but only a part of it. Another important contribution of this work is the automatic approach selection because, on the one hand, it is known that just one approach is not sufficient for the recognition of all objects. On the other hand, neither the video portals nor their users can be supposed to select an appropriate recognition approach for each object-of-interest. We have shown that the selected approaches significantly outperform a baseline bag-of-features approach for most examples and that different recognition approaches are selected for different object categories. For instance, face detection approaches are frequently selected for the annotation of persons. Furthermore, we want to point out that our evaluations (as well as the evaluations of related works) show that current recognition approaches cannot perform the annotation of any object like the human visual system. However, we believe that the proposed framework will still be useful, if not more effective, when computer vision techniques have improved further. In this context, automatic approach selection will be especially important to support novel recognition approaches that are tailor-made for specific object types. Moreover, we made some additional observations in our experiments that might be interesting for video portals. Firstly, the objects-of-interest that are suitable for annotation are restricted by their minimum size and by the number of object instances. Only those objects that take at least one third of a keyframe were selected and it takes an exceptionally long time to annotate objects that are only shown in one video scene. Secondly, we found that it is difficult to annotate unknown videos in a way that the selected objects and their labels are not superficial. Thus, we recommend to annotate objects shortly before or after the video upload. Thirdly, we noted it as preferable to select keyframes that show the object-of-interest with the highest amount of variability (e.g., the same person with different clothing, haircuts, and from different viewpoints) instead of simply choosing the first correct suggestion if manypositive keyframes are suggested by the prototype.