Semantic image segmentation with fused CNN features

被引:10
|
作者
Geng H.-Q. [1 ]
Zhang H. [1 ]
Xue Y.-B. [1 ]
Zhou M. [1 ]
Xu G.-P. [1 ]
Gao Z. [1 ]
机构
[1] Key Laboratory of Computer Vision and System of Ministry of Education, Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin
基金
中国国家自然科学基金;
关键词
D O I
10.1007/s11801-017-7086-6
中图分类号
学科分类号
摘要
Semantic image segmentation is a task to predict a category label for every image pixel. The key challenge of it is to design a strong feature representation. In this paper, we fuse the hierarchical convolutional neural network (CNN) features and the region-based features as the feature representation. The hierarchical features contain more global information, while the region-based features contain more local information. The combination of these two kinds of features significantly enhances the feature representation. Then the fused features are used to train a softmax classifier to produce per-pixel label assignment probability. And a fully connected conditional random field (CRF) is used as a post-processing method to improve the labeling consistency. We conduct experiments on SIFT flow dataset. The pixel accuracy and class accuracy are 84.4% and 34.86%, respectively. © 2017, Tianjin University of Technology and Springer-Verlag GmbH Germany.
引用
收藏
页码:381 / 385
页数:4
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