计算机应用与软件2018，Vol.35Issue(2)：284-288,5.DOI:10.3969/j.issn.1000-386x.2018.02.051
基于神经网络的图像弱监督语义分割算法
WEAKLY SUPERVISED IMAGE SEMANTIC SEGMENTATION ALGORITHM BASED ON NEURAL NETWORK
顾攀 1张烽栋2
作者信息
- 1. 复旦大学计算机科学技术学院 上海 201203
- 2. 上海市智能信息处理重点实验室(复旦大学) 上海 201203
- 折叠
摘要
Abstract
Category activating feature map algorithm is an algorithm which is able to explore the feature map according to specific category and train it by weakly supervised sample,and the semantic information extracted could be provided to other detection or location mission.Thus, an image semantic segmentation algorithm which is calculated by neural network is proposed, the model could be obtained by training the neural network by weakly supervised training data. This algorithm calculated the rough region by semantic segmentation which combined the feature image from neural network with the network parameter,then obtained the more accurate image semantic segmentation by passing back the semantic information.Extensive experiments show that the proposed method has competitive performance on many datasets.Furthermore,we visualize some results to explain the details of the proposed algorithm.关键词
语义分割/深度学习/神经网络/计算机视觉Key words
Semantic segmentation/Deep learning/Neural network/Computer vision分类
信息技术与安全科学引用本文复制引用
顾攀,张烽栋..基于神经网络的图像弱监督语义分割算法[J].计算机应用与软件,2018,35(2):284-288,5.
