论文标题
土耳其：用于众包注意数据的基于网络的工具箱
TurkEyes: A Web-Based Toolbox for Crowdsourcing Attention Data
论文作者
论文摘要
眼动提供了有关观众发现与手头任务最显着，有趣或相关的图像的哪些部分的洞察力。不幸的是，眼睛跟踪数据（通常是关注的常用代理）很麻烦地收集。在这里，我们探索了一种替代方案：一个全面的基于网络的工具箱，用于众包视觉关注。我们从文献中的四个主要捕获方法学方法中得出。 Zoommaps是一种小说的“基于变焦”的界面，可在手机上捕获观看。 CodeCharts是一种“自我报告”方法，可在精确的观看阶段记录兴趣点。 ImportAnnots是用于选择重要图像区域的“注释”工具，“基于光标的” BubbleView使观众可以单击以脱布一个小区域。我们使用通用分析框架比较这些方法，以便为每个接口开发适当的用例。该工具箱和我们的分析为如何在没有眼动物的情况下大规模收集注意力数据提供了蓝图。
Eye movements provide insight into what parts of an image a viewer finds most salient, interesting, or relevant to the task at hand. Unfortunately, eye tracking data, a commonly-used proxy for attention, is cumbersome to collect. Here we explore an alternative: a comprehensive web-based toolbox for crowdsourcing visual attention. We draw from four main classes of attention-capturing methodologies in the literature. ZoomMaps is a novel "zoom-based" interface that captures viewing on a mobile phone. CodeCharts is a "self-reporting" methodology that records points of interest at precise viewing durations. ImportAnnots is an "annotation" tool for selecting important image regions, and "cursor-based" BubbleView lets viewers click to deblur a small area. We compare these methodologies using a common analysis framework in order to develop appropriate use cases for each interface. This toolbox and our analyses provide a blueprint for how to gather attention data at scale without an eye tracker.
