多标签交互式图像分割的高阶条件随机场

T. Nguyen, N. Pham, Trung-Thien Tran, H. Le
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引用次数: 4

摘要

在本文中,我们提出了一种有效的方法来解决多标签交互图像分割问题,该方法采用高阶条件随机场模型将超像素作为高阶能量。人们利用CRF模型进行无监督分割已经很多年了,但是它需要训练集来提供必要的信息。因此,对于各种图像上下文和分类,无监督策略是相当有限的。出于这个原因,用户交互似乎是不可避免的,它可以帮助我们根据开发CRF的观点来解决多标签分割的难题。在MSRC和Berkeley数据集上进行了实验,并与原始的条件随机场框架进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Higher Order Conditional Random Field for Multi-Label Interactive Image Segmentation
In this paper, we propose the efficient approach to tackle the multi-label interactive image segmentation issue by applying the higher order Conditional Random Fields model which associates superpixel as higher order energy. People did take advantage of CRF model for unsupervised segmentation for years, but it requires training set for providing neccessary information. Therefore, unsupervised strategy is fairly restrictive for the variety of image contexts and categorizations. For this reason, the user interaction seems inevitable to help us address the multi- label segmentation's riddle in accordance with exploiting CRF perspectives. The promising experiments are conducted in MSRC and Berkeley dataset comparing with the original Conditional Random Fields framework.
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