progressive cut
Recently, interactive image cutout technique becomes prevalent for image segmentation problem due to its easy-to-use nature. However, most existing stroke-based interactive object cutout system did not consider the user intention inherent in the user interaction process. Strokes in sequential steps are treated as a collection rather than a process, and only the color information of the additional stroke is used to update the color model in the graph cut framework. Accordingly, unexpected fluctuation effect may occur during the process of interactive object cutout. In fact, each step of user interaction reflects the user's evaluation of previous result and his/her intention. By analyzing the user's intention behind the interaction, we propose a progressive cut algorithm, which explicitly models the user's intention into a graph cut framework for the object cutout task. Three aspects of user intention are utilized: 1) the color of the stroke indicates the kind of change s/he expects, 2) the location of the stroke indicates the region of interest, 3) the relative position between the stroke and the previous result indicates the segmentation error. By incorporating such information into the cutout system, the new algorithm removes the unexpected fluctuation effect of existing stroke-based graph-cut methods, and thus provides the user a more controllable result with fewer strokes and faster visual feedback. Experiments and user study show the strength of progressive cut in accuracy, speed, controllability, and user experience.
graph cuts user attention foreground extraction interactive image segmentation