Pohlmeyer Eric A, Jangraw David C, Wang Jun, Chang Shih-Fu, Sajda Paul
Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:138-41. doi: 10.1109/IEMBS.2010.5627403.
Our group has been investigating the development of BCI systems for improving information delivery to a user, specifically systems for triaging image content based on what captures a user's attention. One of the systems we have developed uses single-trial EEG scores as noisy labels for a computer vision image retrieval system. In this paper we investigate how the noisy nature of the EEG-derived labels affects the resulting accuracy of the computer vision system. Specifically, we consider how the precision of the EEG scores affects the resulting precision of images retrieved by a graph-based transductive learning model designed to propagate image class labels based on image feature similarity and sparse labels.
我们的团队一直在研究脑机接口（BCI）系统的开发，以改善向用户传递信息的方式，特别是基于吸引用户注意力的内容对图像进行分类的系统。我们开发的其中一个系统使用单次试验脑电图分数作为计算机视觉图像检索系统的噪声标签。在本文中，我们研究了脑电图衍生标签的噪声性质如何影响计算机视觉系统的最终准确性。具体而言，我们考虑脑电图分数的精度如何影响通过基于图像特征相似性和稀疏标签传播图像类别标签的基于图的转导学习模型检索的图像的最终精度。
