User driven sparse point-based image segmentation

Reducing the amount of user driven input for interactive image segmentation enables faster and more precise foreground extraction of objects. A sparse collection of labeled seed points sampled over image regions can be quickly provided by the user using a few mouse clicks. Seed points are used for training an Elastic Body Spline classifier mapping function. We evaluate the efficiency and accuracy of user defined point inputs compared to fully manual boundary drawing that can be time consuming and automatic image segmentation methods that may not have sufficient accuracy. We show that using an average of just 8 labeled pixels (i.e. sparse set of seed points) the proposed EBS foreground-background thresholding method can achieve 90 percent accuracy compared to manual ground truth on the Berkeley BSDS500 benchmark.