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机译:学习具有标签一致性的判别距离度量进行场景分类
School of Mathematical Sciences, Beijing Normal University, Beijing, China;
Beijing Key Laboratory of Environmental Remote Sensing and Digital City, School of Geography, Faculty of Geographical Science, Beijing Normal University, Beijing, China;
Department of Geoinfomatics, Central South University, Changsha, China;
Department of Automation, Tsinghua University, Beijing, China;
School of Mathematical Sciences, Beijing Normal University, Beijing, China;
Beijing Key Laboratory of Environmental Remote Sensing and Digital City, School of Geography, Faculty of Geographical Science, Beijing Normal University, Beijing, China;
School of Mathematical Sciences, Beijing Normal University, Beijing, China;
Beijing Key Laboratory of Environmental Remote Sensing and Digital City, School of Geography, Faculty of Geographical Science, Beijing Normal University, Beijing, China;
Beijing Key Laboratory of Environmental Remote Sensing and Digital City, School of Geography, Faculty of Geographical Science, Beijing Normal University, Beijing, China;
Measurement; Feature extraction; Encoding; Optimization; Spatial resolution; Remote sensing; Learning systems;
机译:区分性特征学习和区域一致性激活,可实现强大的场景标记
机译:多标签分类的学习距离度量
机译:DLANet:用于场景分类的基于多分类学习的判别特征学习网络
机译:图像到类距离的多标签学习,用于场景分类和图像注释
机译:从文本和图像中学习:部分标记数据的生成模型和判别模型。
机译:具有Fisher判别和癫痫eEG信号分类的Fisher判别和全局结构约束的自动加权多视图判别度量学习方法
机译:通过图像到类的距离进行多标签学习,用于场景分类和图像标注
机译:利用学习VQ框架进行高光谱图像分类的判别和紧凑字典设计。