Glimpse: A gaze-based measure of temporal salience

Temporal salience considers how visual attention varies over time. Although visual salience has been widely studied from a spatial perspective, its temporal dimension has been mostly ignored, despite arguably being of utmost importance to understand the temporal evolution of attention on dynamic contents. To address this gap, we proposed GLIMPSE, a novel measure to compute temporal salience based on the observer-spatio-temporal consistency of raw gaze data. The measure is conceptually simple, training free, and provides a semantically meaningful quantification of visual attention over time. As an extension, we explored scoring algorithms to estimate temporal salience from spatial salience maps predicted with existing computational models. However, these approaches generally fall short when compared with our proposed gaze-based measure. GLIMPSE could serve as the basis for several downstream tasks such as segmentation or summarization of videos. GLIMPSE’s software and data are publicly available

Towards a reliable collection of eye-tracking data for image quality research: challenges, solutions and applications

Image quality assessment potentially benefits from the addition of visual attention. However, incorporating aspects of visual attention in image quality models by means of a perceptually optimized strategy is largely unexplored. Fundamental challenges, such as how visual attention is affected by the concurrence of visual signals and their distortions; whether visual attention affected by distortion or that driven by the original scene only should be included in an image quality model; and how to select visual attention models for the image quality application context, remain. To shed light on the above unsolved issues, designing and performing eye-tracking experiments are essential. Collecting eye-tracking data for the purpose of image quality study is so far confronted with a bias due to the involvement of stimulus repetition. In this paper, we propose a new experimental methodology to eliminate such inherent bias. This allows obtaining reliable eye-tracking data with a large degree of stimulus variability. In fact, we first conducted 5760 eye movement trials that included 160 human observers freely viewing 288 images of varying quality. We then made use of the resulting eye-tracking data to provide insights into the optimal use of visual attention in image quality research. The new eye-tracking data are made publicly available to the research community

A deep-learning based feature hybrid framework for spatiotemporal saliency detection inside videos

Although research on detection of saliency and visual attention has been active over recent years, most of the existing work focuses on still image rather than video based saliency. In this paper, a deep learning based hybrid spatiotemporal saliency feature extraction framework is proposed for saliency detection from video footages. The deep learning model is used for the extraction of high-level features from raw video data, and they are then integrated with other high-level features. The deep learning network has been found extremely effective for extracting hidden features than that of conventional handcrafted methodology. The effectiveness for using hybrid high-level features for saliency detection in video is demonstrated in this work. Rather than using only one static image, the proposed deep learning model take several consecutive frames as input and both the spatial and temporal characteristics are considered when computing saliency maps. The efficacy of the proposed hybrid feature framework is evaluated by five databases with human gaze complex scenes. Experimental results show that the proposed model outperforms five other state-of-the-art video saliency detection approaches. In addition, the proposed framework is found useful for other video content based applications such as video highlights. As a result, a large movie clip dataset together with labeled video highlights is generated