Context-based global multi-class semantic image segmentation by wireless multimedia sensor networks
Context-based global multi-class semantic image segmentation by wireless multimedia sensor networks
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DOI：
10.1007/s10462-013-9394-y
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发表时间：
2015-04
影响因子：
12
通讯作者：
Chen Wei;Xiaorong Jiang;Zhibo Tang;Wang Qian;N. Fan
中科院分区：
文献类型：
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作者：
Chen Wei;Xiaorong Jiang;Zhibo Tang;Wang Qian;N. Fan
使用上下文来辅助对象检测在计算机视觉研究人员中越来越受欢迎。我们的物理世界是结构化的，我们作为人类的感知不会忽视上下文信息。在本文中，我们提出了一个能够在上下文下同时检测和分割不同类对象的框架。上下文被纳入我们的模型作为像素之间的长距离成对相互作用，这对标签施加了先验。远程交互在计算机视觉文献中很少使用，我们展示了如何使用它们来编码分割中的上下文信息。我们的框架制定的多类图像分割任务作为一个能量最小化的问题，并找到一个全局最优的解决方案，在一定条件下，使用一个单一的图形切割。我们在两个公开的数据集上实验评估了我们的模型的性能：MSRC-1和CorelB数据集。我们的研究结果表明，我们的模型的多类分割问题的适用性。
Using context to aid object detection is becoming more popular among computer vision researchers. Our physical world is structured, and our perception as human beings does not neglect contextual information. In this paper, we propose a framework that is able to simultaneously detect and segment objects of different classes under context. Context is incorporated into our model as long-range pairwise interactions between pixels, which impose a prior on the labeling. Long-range interactions have seen seldom use in the computer vision literature, and we show how to use them to encode contextual information in our segmentation. Our framework formulates the multi-class image segmentation task as an energy minimization problem and finds a globally optimal solution under certain conditions using a single graph cut. We experimentally evaluate performance of our model on two publicly available datasets: the MSRC-1 and the CorelB datasets. Our results show the applicability of our model to the multi-class segmentation problem.