在OPT查看期间用于专业知识分类的深度语义凝视嵌入和扫描路径比较
Deep semantic gaze embedding and scanpath comparison for expertise classification during OPT viewing
摘要：
对指示专业行为的眼动进行建模在用户评估中具有决定性意义。然而，任务语义对注视行为的影响是无可争议的。我们提出了一种新的方法，凝视扫描路径比较，结合卷积神经网络（CNN）处理场景信息的固定水平。图像补丁链接到各自的固定被用作输入的CNN和由此产生的特征向量提供必要的扫描路径similaritycomparison.We的时间和空间的凝视信息评估我们提出的方法从专家和新手牙医解释牙科X光片使用本地对齐相似性得分的凝视数据。我们的方法是能够区分专家与新手93%的准确率，同时结合图像语义。此外，我们的扫描路径比较使用图像补丁功能有可能纳入任务语义从各种任务
英文摘要：
Modeling eye movement indicative of expertise behavior is decisive in user evaluation. However, it is indisputable that task semantics affect gaze behavior. We present a novel approach to gaze scanpath comparison that incorporates convolutional neural networks (CNN) to process scene information at the fixation level. Image patches linked to respective fixations are used as input for a CNN and the resulting feature vectors provide the temporal and spatial gaze information necessary for scanpath similarity comparison.We evaluated our proposed approach on gaze data from expert and novice dentists interpreting dental radiographs using a local alignment similarity score. Our approach was capable of distinguishing experts from novices with 93% accuracy while incorporating the image semantics. Moreover, our scanpath comparison using image patch features has the potential to incorporate task semantics from a variety of tasks
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
