DISCOV: A framework for discovering objects in video
This paper presents a probabilistic framework for discovering objects in video. The video can switch between different shots, the unknown objects can leave or enter the scene at multiple times, and the background can be cluttered. The framework consists of an appearance model and a motion model. The appearance model exploits the consistency of object parts in appearance across frames. We use maximally stable extremal regions as observations in the model and hence provide robustness to object variations in scale, lighting and viewpoint. The appearance model provides location and scale estimates of the unknown objects through a compact probabilistic representation. The compact representation contains knowledge of the scene at the object level, thus allowing us to augment it with motion information using a motion model. This framework can be applied to a wide range of different videos and object types, and provides a basis for higher level video content analysis tasks. We present applications of video object discovery to video content analysis problems such as video segmentation and threading, and demonstrate superior performance to methods that exploit global image statistics and frequent itemset data mining techniques.
video object discovery video segmentation multimedia data mining unsupervised learning