论文标题
在OPT查看期间，深层语义注视嵌入和扫描PATH比较专业知识分类
Deep semantic gaze embedding and scanpath comparison for expertise classification during OPT viewing
论文作者
论文摘要
在用户评估中，建模眼动运动指示专业知识行为是决定性的。但是，毫无疑问，任务语义会影响凝视行为。我们提出了一种新颖的凝视扫描路线比较方法，该方法将卷积神经网络（CNN）结合在一起，以在固定级别处理场景信息。链接到各自固定的图像贴片被用作CNN的输入，所得的特征向量提供了扫描路径相似性比较所需的时间和空间凝视信息。我们评估了我们对专家和新手牙科牙医的凝视数据的拟议方法来解释牙科摄影，从而使用地方一致性相似性分数来解释牙科摄影。我们的方法能够在合并图像语义的同时将专家与精度为93％的新手区分开。此外，我们使用图像补丁功能的扫描Path比较有可能从各种任务中结合任务语义
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
