应用信息论和熵消除跟踪结果中的误差
Applying information theory and entropy to eliminate errors in mouse-tracking results
摘要：
对计算机系统用户的鼠标跟踪代表了一种较便宜的，但也是眼睛跟踪的更适用的替代方案。鼠标跟踪的主要缺点是表现为实际眼睛注视位置和鼠标光标位置之间的差异的误差。本文提出了一种自动修正鼠标跟踪误差的方法。我们的方法借鉴了信息论，并采用香农熵。该方法基于计算网页的视觉表示的熵，即，我们量化了各种位置的信息潜在价值。由此获得的信息与鼠标光标在每个位置所花费的累积时间间隔配对。通过这种方式，我们识别出与眼睛注视位置不匹配的光标位置。为了验证我们的方法的有效性，我们通过以下方式比较了眼睛注视和鼠标光标热图：我们计算了两者之间的相关系数;我们计算了它们的重心之间的欧几里得距离;我们进行了视觉比较。
英文摘要：
Mouse-tracking of computer system users represents a less expensive, but also a far more applicable alternative to eye-tracking. The main disadvantage of mouse-tracking are errors manifested as discrepancies between the actual eye-gaze position and the mouse cursor position. This paper presents a method for automated correction of errors arising in mouse-tracking. Our approach draws upon information theory and employs Shannon entropy. The method is based on calculating the entropy of a visual representation of a Web page, i.e., we quantify information potential values of various positions. Information obtained, thereby, is paired with cumulative time intervals, spent by the mouse cursor in each position. In this way, we identify cursor positions that do not match eye-gaze positions. To verify the effectiveness of our method, we compared the eye gaze and mouse cursor heat maps in the following ways: We calculated the coefficient of correlation between the two; we computed Euclidean distance between their centers of gravity; and we performed visual comparison.
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