MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation

Jiajun Wu, Yibiao Zhao, Jun-Yan Zhu, Siwei Luo, Zhuowen Tu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 256-263

Abstract


Interactive segmentation, in which a user provides a bounding box to an object of interest for image segmentation, has been applied to a variety of applications in image editing, crowdsourcing, computer vision, and medical imaging. The challenge of this semi-automatic image segmentation task lies in dealing with the uncertainty of the foreground object within a bounding box. Here, we formulate the interactive segmentation problem as a multiple instance learning (MIL) task by generating positive bags from pixels of sweeping lines within a bounding box. We name this approach MILCut. We provide a justification to our formulation and develop an algorithm with significant performance and efficiency gain over existing state-of-the-art systems. Extensive experiments demonstrate the evident advantage of our approach.

Related Material


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[bibtex]
@InProceedings{Wu_2014_CVPR,
author = {Wu, Jiajun and Zhao, Yibiao and Zhu, Jun-Yan and Luo, Siwei and Tu, Zhuowen},
title = {MILCut: A Sweeping Line Multiple Instance Learning Paradigm for Interactive Image Segmentation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2014}
}