## 1 Introduction
One interesting finding of this work is that tags are generally not used to enhance the video description but to improve the video popularity. [27] further discovered, that people have a higher motivation to annotate their content when they get the right incentives and affordances for annotation, which means that poorly annotated content is often a consequence of inappropriate annotation tools. Social tagging systems are described in [28] together with a taxonomy to compare different tagging architectures, as they have done it for Flickr and Delicious [29, 30]. Another initiative to improve video annotations comes from annotation games, such as OntoTube and Yahoo's VideoTagGame, where users are motivated to tag videos for fame [31]. Content-based analysis techniques have been investigated in many works for the use in video annotation and retrieval. Amongst other things, researchers proposed ways to generate better and more annotations using tag recommendation, tag propagation, and tag ordering [32]. Although a large number of retrieval techniques and systems exist that use low-level content features independently or combined with text queries (see Section 2), content based analysis techniques are not presented in current video sharing portals. The proposed object-of-interest annotation framework in this work tries to learn from related systems and focuses on solutions with a high potential for industrial applicability. Thus, this framework represents an interface between video portals and computer vision research targeting object recognition. The framework further offers the possibility to improve video sharing portals in regard to their capability of scene retrieval and object based text queries. It exploits object recognition approaches without presenting new methods or fixed analysis workflows. Recognition approaches of all kinds including global and local detector-descriptor chains, matching strategies and machine learning techniques can be integrated and the framework can be easily extended to incorporate computer vision advances. Moreover, we designed this framework for the use in various video platforms instead of proposing another stand-alone system. Despite its flexibility in matter of recognition approach extension and its compatibility with existing video portals, a few cornerstones mark the framework's efficient nature: Firstly, videos are represented by a few keyframes per scene. Secondly, object annotations and low-level features are stored in a simple and compact form with sufficient information to answer text queries on scene level. Thirdly, an appropriate recognition approach is automatically selected for each annotated object. Furthermore, the framework was designed for the use in large-scale video collections with distributed analysis on multi-core and multi-machine architectures. Figure 1 shows a high-level perspective of the framework in combination with the roles of video portals, their users, and computer vision engineers as well as an overview of the proposed object-of-interest annotation. This annotation process first pre-processes all videos of the portal to select relevant keyframes and to extract visual features. User interaction is then required to initiate the annotation of new objects in uploaded videos starting from a single example. In this process, keyframes that are supposed to contain the same object are returned to the user for feedback. After this training data generation, the object is automatically annotated in all videos of the collection. The relevance of scene and object level retrieval results should then be visible to the user at the first glance, as shown for the query \"cow\" in Figure 1. The evaluations of this work are focused on the object-of-interest  
annotation in real-world scenarios and on automatic approach selection to show the capabilities of the proposed framework. The remainder of this work is structured as follows. Section 2 reviews the related work in the area of object-based video annotation, object recognition, and recognition infrastructures.