Stratification-Based Keyframe Cliques for Effective and Efficient Video Representation
As there is an exponential increase of web videos, it is time-consuming to get a query result from the tremendous data. An effective and efficient video management system is in urgent need. To increase the efficiency of video retrieval and storage, the most widely used methods are indexing schemes, such as locality sensitive hashing (LSH). However, it is more essential to represent the video itself compactly. In this paper, we propose a strategy to generate stratification-based keyframe cliques (SKCs) for video description, which are more compact and informative than frames or keyframes. The new representations are scalable for different retrieval tasks due to the ranking of SKCs. To further accelerate the retrieval speed, only top SKCs will be used; meanwhile, the searching results are still satisfactory. Experiments are conducted on TRECVID dataset as well as web video dataset. Results show that our proposed SKCs are more succinct and informative for video retrieval and management.