SUPERCUT: An accurate and effective interactive image segmentation algorithm

Qingsong Zhu, Ling Shao, Zhan Song, Yaoqin Xie

Research output: Contribution to conferencePaper

Abstract

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.
Original languageEnglish
DOIs
Publication statusPublished - Sep 2013
EventICIP 2013 - 20th IEEE International Conference on Image Processing - Melbourne, Australia
Duration: 1 Sep 2013 → …

Conference

ConferenceICIP 2013 - 20th IEEE International Conference on Image Processing
Period1/09/13 → …

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