机译:交通场景识别深度多分类器融合
Department of Computer Science and Software Engineering Xi'an Jiaotong-liverpool University Suzhou Jiangsu China;
The Institute of Electronics Communications and Information Technology Queen's University Belfast Belfast UK;
Department of Electrical Engineering and Electronic University of Liverpool Liverpool UK;
School of Computer and Data Engineering Ningbo Institute of Technology Zhejiang University Ningbo Zhejiang China;
Traffic scene recognition; Convolutional neural networks; Multi-classifier fusion;
机译:交通场景图像中小型交通灯识别的深度学习框架
机译:基于分类器融合的自适应深度共同发生特征学习,用于遥感场景分类
机译:基于多特征融合和ELM分类器的交通标志识别
机译:具有多特征加权融合的分层分类器,用于场景识别
机译:场景理解的神经模型:场景搜索,学习和识别中多尺度基于空间和基于特征的注意力。
机译:基于特征和分数融合的虹膜识别多分类器选择
机译:基于分类器融合的自适应深度共同发生特征学习,用于遥感场景分类