Seed growing for interactive image segmentation using SVM classification with geodesic distance
S. Park, H.S. Lee, Jinhwan Kim · Electronics Letters · 2016
In an interactive image segmentation, the quantity of a user‐given seed is known to affect the segmentation accuracy. In this Letter, we propose a seed‐growing method expanding the quantity of a seed to reduce the bias of the given seed and improve the segmentation accuracy. To grow the given seed, a supervised classification framework with geodesic distance features is proposed. From a single input image, a support vector machine (SVM) classifier is trained on the seed superpixels of an input image. Other non‐seed superpixels are then classified into object, background and non‐seed regions by the trained classifier. In experiments, the proposed method showed promising results by improving the segmentation accuracy of existing segmentation methods in public benchmark datasets.