Interactive image segmentation by matching attributed relational graphs

A model-based graph matching approach is proposed for interactive image segmentation. It starts from an over-segmentation of the input image, exploiting color and spatial information among regions to propagate the labels from the regions marked by the user-provided seeds to the entire image. The region merging procedure is performed by matching two graphs: the input graph, representing the entire image; and the model graph, representing only the marked regions. The optimization is based on discrete search using deformed graphs to efficiently evaluate the spatial information. Note that by using a model-based approach, different interactive segmentation problems can be tackled: binary and multi-label segmentation of single images as well as of multiple similar images. Successful results for all these cases are presented, in addition to a comparison between our binary segmentation results and those obtained with state-of-the-art approaches. An implementation is available at http://structuralsegm.sourceforge.net/.

► It starts from an oversegmentation to propagate the labels by region merging.
► It is trivially extensible from binary to multi-label segmentation.
► Spatial relations are exploited besides color information.
► It can be combined with the connectivity constraint to reduce the user effort.
► By including a boundary refinement step, it produced state-of-the-art results.