Saliency Prediction for Mobile User Interfaces
Saliency Prediction for Mobile User Interfaces
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DOI：
10.1109/wacv.2018.00171
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Prakhar Gupta;Shubh Gupta;Ajaykrishnan Jayagopal;Sourav Pal;R. Sinha
中科院分区：
文献类型：
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作者：
Prakhar Gupta;Shubh Gupta;Ajaykrishnan Jayagopal;Sourav Pal;R. Sinha
我们介绍了移动的用户界面的显着性预测模型。除了能够执行各种任务的自然图像之外，移动的界面还可以包括诸如按钮和文本的元素。自然图像中的显著性是一个很好的研究课题。然而，考虑到构成移动的界面的差异以及这些设备的使用环境，我们假设移动的界面图像的显着性预测需要一种新的方法。移动的界面设计涉及对元素的操作，即界面的构建块。我们首先从移动的设备上收集了一个免费观看任务的眼睛注视数据。使用这些数据，我们开发了一种新的基于自动编码器的多尺度深度学习模型，该模型在移动的界面元素级别提供显着性预测。与为自然图像开发的显着性预测方法相比，我们表明我们的方法在一系列既定指标上表现得更好。
We introduce models for saliency prediction for mobile user interfaces. A mobile interface may include elements like buttons and text in addition to natural images which enable performing a variety of tasks. Saliency in natural images is a well studied topic. However, given the difference in what constitutes a mobile interface, and the usage context of these devices, we postulate that saliency prediction for mobile interface images requires a fresh approach. Mobile interface design involves operating on elements, the building blocks of the interface. We first collected eye-gaze data from mobile devices for a free viewing task. Using this data, we develop a novel autoencoder based multi-scale deep learning model that provides saliency prediction at the mobile interface element level. Compared to saliency prediction approaches developed for natural images, we show that our approach performs significantly better on a range of established metrics.