条件随机场像素建模与深度特征融合的目标区域分割算法

Object Region Segmentation Using Deep Features and Pixel Modeling by Conditional Random Fields

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.