Image segmentation refers to the task of grouping image pixels into a meaningful partition such that pixels falling in the same group are similar to each other, and different than those in other groups, in terms of a perceptually meaningful similarity measure. Segmentation often is used as a pre-processing step before higher level image processing or understanding. Our work in this paper is based on constrained spectral clustering that iteratively incorporates user feedback by propagating it through the calculated affinities. The original framework does not scale well to large data sets, and hence is not straightforward to apply to interactive image segmentation. In order to address this issue, we adopt advanced numerical methods for eigen-decomposition implemented over a sub sampling scheme. Our key innovation, however, is an active learning strategy that chooses pair wise queries to present to the user in order to increase the rate of learning from the feedback. Performance evaluation is carried out on the Berkeley segmentation and Graz-02 image data sets, confirming that convergence to high accuracy levels is realizable in relatively few iterations.
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