结合CRF与ShapeBM形状先验的图像标记

王浩; 郭立君; 王亚东; 张荣

doi:10.11959/j.issn.1000-0801.2017004

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结合CRF与ShapeBM形状先验的图像标记

研究与开发 | 更新时间：2024-06-05

- 结合CRF与ShapeBM形状先验的图像标记
- CRF combined with ShapeBM shape priors for image labeling
- 电信科学 2017年33卷第1期页码：67-76
- 作者机构：
- 作者简介：
  
  [ "王浩（1992-），男，宁波大学信息科学与工程学院硕士生，主要研究方向为计算机视觉与模式识别。" ]
  [ "郭立君（1970-），男，博士，宁波大学教授，主要研究方向为计算机视觉与模式识别、移动互联网及其应用。" ]
  [ "王亚东（1990-），男，宁波大学信息科学与工程学院硕士生，主要研究方向为计算机视觉与模式识别。" ]
  [ "张荣（1974-），女，博士，宁波大学副教授，主要研究方向为计算机视觉与信息安全。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61175026);浙江省自然科学基金资助项目;Zhejiang Provincial Natural Science Foundation of China(LY17F030002);宁波市自然科学基金资助项目;Ningbo Municipal Natural Science Foundation(2014A610031);浙江省“信息与通信工程”重中之重学科开放基金资助项目;Open Research Fund of Zhejiang First-Foremost Key Subject-Information and Communications Engineering of China(xkxl1516);浙江省“信息与通信工程”重中之重学科开放基金资助项目;Open Research Fund of Zhejiang First-Foremost Key Subject-Information and Communications Engineering of China(xkxl1521)
- DOI：10.11959/j.issn.1000-0801.2017004
  中图分类号： TP391
- 网络出版日期：2017-01，
  
  纸质出版日期：2017-01-15
- 稿件说明：
移动端阅览
王浩, 郭立君, 王亚东, 等. 结合CRF与ShapeBM形状先验的图像标记[J]. 电信科学, 2017,33(1):67-76.

Hao WANG, Lijun GUO, Yadong WANG, et al. CRF combined with ShapeBM shape priors for image labeling[J]. Telecommunications science, 2017, 33(1): 67-76.
王浩, 郭立君, 王亚东, 等. 结合CRF与ShapeBM形状先验的图像标记[J]. 电信科学, 2017,33(1):67-76. DOI： 10.11959/j.issn.1000-0801.2017004.

Hao WANG, Lijun GUO, Yadong WANG, et al. CRF combined with ShapeBM shape priors for image labeling[J]. Telecommunications science, 2017, 33(1): 67-76. DOI： 10.11959/j.issn.1000-0801.2017004.

摘要

条件随机场（CRF）是一种强大的图像标记模型，适合描述图像相邻区域间（例如超像素）的相互作用。然而，CRF没有考虑标记对象的全局约束。对象的整体形状可以作为对象标记的一种全局约束，利用形状玻尔兹曼机（ShapeBM）在建模对象的整体形状方面的优势，提出了一种CRF与ShapeBM相结合的标记模型。标记模型建立在超像素的基础上，并通过pooling技术在CRF的超像素层与ShapeBM的输入层间建立对应关系，增强了 CRF 与 ShapeBM 结合的有效性，提高了标记准确率。在 Penn-Fudan Pedestrians 数据集和 Caltech-UCSD Birds 200数据集上的实验结果表明，联合模型明显地改善了标记结果。

Abstract

Conditional random field (CRF) is a powerful model for image labeling

it is particularly well-suited to model local interactions among adjacent regions (e.g.superpixels).However

CRF doesn't consider the global constraint of objects.The overall shape of the object is used as a global constraint

the ShapeBM can be taken advantage of modeling the global shape of object

and then a new labeling model that combined the above two types of models was presented.The combination of CRF and ShapeBM was based on the superpixels

through the pooling technology was wed to establish the corresponding relationship between the CRF superpixel layer and the ShapeBM input layer.It enhanced the effectiveness of the combination of CRF and ShapeBM and improved the accuracy of the labeling.The experiments on the Penn-Fudan Pedestrians dataset and Caltech-UCSD Birds 200 dataset demonstrate that the model is more effective and efficient than others.

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references

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