Interactive video segmentation based on quasi-flat zones

Video data is continuously increasing in personal databases and Web repositories. To exploit such data, a prior segmentation is often needed in order to get the objects-of-interest to be further processed. However, the segmentation of a given video is often not unique and indeed depends on user needs. Personalized segmentation may be achieved using interactive methods but only if their computational cost stays reasonable to enable user interactivity. We address here the problem of interactive video segmentation and introduce a 2-step segmentation scheme: 1) offline processing to automatically extract quasi-flat zones from video data, and 2) online processing to interactively gather quasi-flat zones and build objects-of-interest. Our approach is able to deal with multiple objects, robust to errors introduced by the automatic segmentation step, and does not require to perform again the whole segmentation process each time the user provides some feedback.