Hybrid Approaches for Interactive Image Segmentation Using the Live Markers Paradigm

Interactive image segmentation methods normally rely on cues about the foreground imposed by the user as region constraints (markers/brush strokes) or boundary constraints (anchor points).These paradigms often have complementary strengths and weaknesses, which can be addressed to improve the interactive experience by reducing the user´s effort. We propose a novel hybrid paradigm based on a new form of interaction called live markers, where optimum boundary-tracking segments are turned into internal and external markers for region-based delineation to effectively extract the object. We present four techniques within this paradigm: 1) LiveMarkers; 2) RiverCut; 3) LiveCut; and 4) RiverMarkers. The homonym LiveMarkers couples boundary-tracking via live-wire-on-the-fly (LWOF) with optimum seed competition by the image foresting transform (IFT-SC). The IFT-SC can cope with complex object silhouettes, but presents a leaking problem on weaker parts of the boundary that is solved by the effective live markers produced by LWOF. Conversely, in RiverCut, the long boundary segments computed by Riverbed around complex shapes provide markers for Graph Cuts by the Min-Cut/Max-Flow algorithm (GCMF) to complete segmentation on poorly defined sections of the object´s border. LiveCut and RiverMarkers further demonstrate that live markers can improve segmentation even when the combined approaches are not complementary (e.g., GCMFs shrinking bias is also dramatically prevented when using it with LWOF). Moreover, since delineation is always region based, our methodology subsumes both paradigms,representing a new way of extending boundary tracking to the 3D image domain, while speeding up the addition of markers close to the object´s boundary - a necessary but time consuming task when done manually. We justify our claims through an extensive experimental evaluation on natural and medical images data sets, using recently proposed robot users for boundary-tracking methods.