One popular approach to interactively segment the foreground object of
interest from an image is to annotate a bounding box that covers the foreground
object. Then, a binary labeling is performed to achieve a refined segmentation.
One major issue of the existing algorithms for such interactive image
segmentation is their preference of an input bounding box that tightly encloses
the foreground object. This increases the annotation burden, and prevents these
algorithms from utilizing automatically detected bounding boxes. In this paper,
we develop a new LooseCut algorithm that can handle cases where the input
bounding box only loosely covers the foreground object. We propose a new Markov
Random Fields (MRF) model for segmentation with loosely bounded boxes,
including a global similarity constraint to better distinguish the foreground
and background, and an additional energy term to encourage consistent labeling
of similar-appearance pixels. This MRF model is then solved by an iterated
max-flow algorithm. In the experiments, we evaluate LooseCut in three
publicly-available image datasets, and compare its performance against several
state-of-the-art interactive image segmentation algorithms. We also show that
LooseCut can be used for enhancing the performance of unsupervised video
segmentation and image saliency detection.

本文提出了一种名为 LooseCut 的新算法，采用 Markov Random Fields 模型，可以处理输入包含前景目标的边界框不太紧的情况，并通过迭代最大流算法解决该模型。LooseCut 可用于增强无监督视频分割和图像显着性检测性能，并在三个公开的图像数据集中表现良好。