The task of interactive image segmentation has attracted a significant attention in recent years. The ultimate goal is to extract an object with as few user interactions as possible. In this paper, we present SUPERCUT, a novel interactive algorithm for foreground object extraction and segmentation in images. In the algorithm, the mean shift algorithm with a boundary confidence prior is introduced to efficiently pre-segment the original image into super-pixels with precise boundary. Secondly, a Bayes decision theory is introduced to model and cluster the super-pixels so as to obtain an initial effective classification of super-pixels. To achieve a more accurate object segmentation result, a boundary refinement using Interactive rectangle box with GMM learning is adopted. Experimental results on a benchmark data set show that the proposed framework is highly effective and can accurately segment a wide variety of natural images with ease.
|Publication status||Published - Sep 2013|
|Event||ICIP 2013 - 20th IEEE International Conference on Image Processing - Melbourne, Australia|
Duration: 1 Sep 2013 → …
|Conference||ICIP 2013 - 20th IEEE International Conference on Image Processing|
|Period||1/09/13 → …|