基于条件随机场和图像分割的显著性检测

钱生; 陈宗海; 林名强; 张陈斌

doi:10.16383/j.aas.2015.c140328

基于条件随机场和图像分割的显著性检测

doi: 10.16383/j.aas.2015.c140328

1.
中国科学技术大学自动化系合肥 230027

基金项目:

国家自然科学基金(61375079)资助

详细信息

作者简介:
钱生 2014年获中国科学技术大学自动化系硕士学位.主要研究方向为计算机视觉,模式识别和生物认知.E-mail:qsheng@mail.ustc.edu.cn

通讯作者:
陈宗海中国科学技术大学自动化系教授.1991年获中国科学技术大学自动化系硕士学位.主要研究方向为复杂系统的建模,仿真与控制,智能机器人和量子信息控制.本文通信作者.E-mail:chenzh@ustc.edu.cn

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- 文章访问数: 3064
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- PDF下载量: 2587
- 被引次数: 0
出版历程
- 收稿日期: 2014-05-09
- 修回日期: 2014-10-27
- 刊出日期: 2015-04-20

Saliency Detection Based on Conditional Random Field and Image Segmentation

1.
Department of Automation, University of Science and Technology of China, Hefei 230027

Funds:

Supported by National Natural Science Foundation of China(61375079)

摘要

摘要: 针对当前常见的显著性方法检测得到的显著性区域边界稀疏不明确、内部不均匀致密等问题,提出了一种基于条件随机场(Condition random field, CRF)和图像分割的显著性检测方法.该方法综合利用边界信息、局部信息以及全局信息,从图像中提取出多种显著性特征;在条件随机场框架下融合这些特征,通过显著性区域与背景区域的区域标注实现显著性区域的粗糙检测;结合区域标注结果和交互式图像分割方法实现显著性区域的精确检测.实验结果表明本文提出的方法能够清晰而准确地提取出图像中的显著性区域,有效提高显著性检测精度.
- 显著性检测 /
- 多特征融合 /
- 条件随机场 /
- 图像分割
Abstract: The problem of sparse and unclear boundary with uneven and non-compact interior existed in the saliency region detected by most saliency detection methods. In order to solve this problem, this paper proposes a saliency detection method based on the conditional random field(CRF) and image segmentation. This method comprehensively utilizes boundary information, local information and global information to extract a variety of salient features from an image. By fusing these features into the framework of conditional random field, a coarse detection for saliency region is realized based on region labeled of saliency region and background region, and then a fine detection for saliency region is achieved through combining the result of region labeled with an interactive image segmentation method. Experimental results show that the proposed approach can clearly and accurately extract saliency regions and improve the detection precision.
- Saliency detection /
- multi-feature fusion /
- conditional random field(CRF) /
- image segmentation

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参考文献(46)

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基于条件随机场和图像分割的显著性检测

doi: 10.16383/j.aas.2015.c140328

作者简介:
钱生 2014年获中国科学技术大学自动化系硕士学位.主要研究方向为计算机视觉,模式识别和生物认知.E-mail:qsheng@mail.ustc.edu.cn

通讯作者:
陈宗海中国科学技术大学自动化系教授.1991年获中国科学技术大学自动化系硕士学位.主要研究方向为复杂系统的建模,仿真与控制,智能机器人和量子信息控制.本文通信作者.E-mail:chenzh@ustc.edu.cn

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Saliency Detection Based on Conditional Random Field and Image Segmentation

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目录

留言板

基于条件随机场和图像分割的显著性检测

doi: 10.16383/j.aas.2015.c140328

作者简介: 钱生 2014年获中国科学技术大学自动化系硕士学位.主要研究方向为计算机视觉,模式识别和生物认知.E-mail:qsheng@mail.ustc.edu.cn

通讯作者: 陈宗海 中国科学技术大学自动化系教授.1991年获中国科学技术大学自动化系硕士学位.主要研究方向为复杂系统的建模,仿真与控制,智能机器人和量子信息控制.本文通信作者.E-mail:chenzh@ustc.edu.cn

计量

出版历程

Saliency Detection Based on Conditional Random Field and Image Segmentation

计量

出版历程

目录

作者简介:
钱生 2014年获中国科学技术大学自动化系硕士学位.主要研究方向为计算机视觉,模式识别和生物认知.E-mail:qsheng@mail.ustc.edu.cn

通讯作者:
陈宗海中国科学技术大学自动化系教授.1991年获中国科学技术大学自动化系硕士学位.主要研究方向为复杂系统的建模,仿真与控制,智能机器人和量子信息控制.本文通信作者.E-mail:chenzh@ustc.edu.cn