Research of animals image semantic segmentation based on deep learning

被引:14
|
作者
Liu, Shouqiang [1 ]
Li, Miao [2 ]
Li, Min [2 ]
Xu, Qingzhen [2 ]
机构
[1] South China Normal Univ, Sch Phys & Telecommun Engn, Guangzhou 510631, Peoples R China
[2] South China Normal Univ, Sch Comp, Guangzhou 510631, Peoples R China
来源
基金
中国国家自然科学基金;
关键词
Caffe; CRF-RNN; FCN; image semantic segmentation; FINITE-TIME STABILITY; BLOW-UP PHENOMENA; COLLOCATION METHODS; GLOBAL EXISTENCE; MINIMAL GRAPH; LEVEL SETS; EQUATION; DISTRIBUTIONS; CONVEXITY; NETWORKS;
D O I
10.1002/cpe.4892
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
It is imperative for us to develop the technology of image semantic segmentation with the increasing demand in the image processing. Nowadays, the development of deep learning is of great significance to the improvement of image segmentation. Furthermore, the paper discussed the relationship between image semantic segmentation and animal image research based on the actual situation, and found that animal image processing technology plays a more important role in the field of protecting precious animals. The end-to-end network training of this paper is consisted of Fully Convolutional Network (FCN) for the front end and Conditional Random Fields as Recurrent Neural Networks (CRF-RNN) for the back end via comparing a variety of research methods. The experiments achieved desired outcome for the semantic segmentation of animal images by utilizing Caffe deep learning framework and explained the implementation details from the aspects of training and testing.
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页数:14
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