Enhancing Interactive Image Segmentation with Automatic Label Set Augmentation

Abstract

We address the problem of having insufficient labels in an interactive image segmentation framework, for which most current methods would fail without further user interaction. To minimize user interaction, we use the appearance and boundary information synergistically. Specifically, we perform distribution propagation on the image graph constructed with color features to derive an initial estimate of the segment labels. Following that, we include automatically estimated segment distributions at “critical pixels” with uncertain labels to improve the segmentation performance. Such estimation is realized by incorporating boundary information using a non-parametric Dirichlet process for modeling diffusion signatures derived from the salient boundaries. Our main contribution is fusion of image appearance with probabilistic modeling of boundary information to segment the whole-object with a limited number of labeled pixels. Our proposed framework is extensively tested on a standard dataset, and is shown to achieve promising results both quantitatively and qualitatively.