摘要
针对已有的基于深度神经网络的目标区域分割算法在目标边界分割效果中存在的问题,提出融合图像像素信息与图像语义信息的目标区域分割算法.首先通过加入注意力模块的深度神经网络提取图像语义级别的信息,得到图像语义级别的全局特征;然后利用条件随机场模型对图像局部区域进行像素级别建模,得到图像的局部细节特征;最后综合利用图像的局部细节特征和图像的全局特征,得到目标区域的分割结果.实验结果表明,与已有的算法相比,该算法能够更好地分割出目标的边界区域,抑制边界区域分割粗糙的问题,得到较准确的目标分割区域.
Existing object region segmentation approaches based on deep convolutional neural networks are inefficient for object boundary segmentation. To address this problem, we propose an object region segmentation algorithm based on pixel level features and semantic level features. Firstly, we extract and get image's semantic information by deep convolutional neural networks with the attention module and get the coarse segmentation results through a specific pixel level classifier. Then, we exploit conditional random fields to model pixel level correlation, thus getting image's local features. Finally, we utilize an optimization framework to fuse the local detail features and global semantic features. Compared with other methods, the experimental results show that our method can improve the segmentation accuracy of boundary and be beneficial for object region segmentation.
作者
李宗民
徐希云
刘玉杰
李华
Li Zongmin;Xu Xiyun;Liu Yujie;Li Hua(College of Computer and Communication Engineering, China University of Petroleum, Qingdao 266520;Computer Application Research Center, Institute of Computing Technology, China Academy of Sciences, Beijing 100190)
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2018年第6期1000-1007,共8页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(61379106
61379082
61227802)
山东省自然科学基金(ZR2013FM036
ZR2015FM011)
关键词
注意力模块
条件随机场
卷积神经网络
目标区域分割
attention module
conditional random fields
convolutional neural networks
object region segmenta-tion