When content-based video retrieval and human computation unite: Towards effective collaborative video search

Although content-based retrieval methods achieved very good results for large-scale video collections in recent years, they still suffer from various deficiencies. On the other hand, plain human perception is a very powerful ability that still outperforms automatic methods in appropriate settings, but is very limited when it comes to large-scale data collections. In this paper, we propose to take the best from both worlds by combining an advanced content-based retrieval system featuring various query modalities with a straightforward mobile tool that is optimized for fast human perception in a sequential manner. In this collaborative system with multiple users, both subsystems benefit from each other: The results of issued queries are used to re-rank the video list on the tablet tool, which in turn notifies the retrieval tool about parts of the dataset that have already been inspected in detail and can be omitted in subsequent queries. The preliminary experiments show promising results in terms of search performance.