Unsupervised Uncertainty Estimation Using Spatiotemporal Cues in Video Saliency Detection

• Published in: IEEE transactions on image processing Vol. 27; no. 6; pp. 2818 - 2827
• Main Authors: Alshawi, Tariq, Zhiling Long, AlRegib, Ghassan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2018
• Subjects: Correlation; Entropy; Estimation; Reliability; Saliency detection; Uncertainty; Unsupervised estimation; video saliency learning; video signal processing; visual attention; Visualization
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2018.2813159

In this paper, we address the problem of quantifying the reliability of computational saliency for videos, which can be used to improve saliency-based video processing algorithms and enable more reliable performance and objective risk assessment of saliency-based video processing applications. Our approach to quantify such reliability is twofold. First, we explore spatial correlations in both the saliency map and the eye-fixation map. Then, we learn the spatiotemporal correlations that define a reliable saliency map. We first study spatiotemporal eye-fixation data from the public CRCNS data set and investigate a common feature in human visual attention, which dictates a correlation in saliency between a pixel and its direct neighbors. Based on the study, we then develop an algorithm that estimates a pixel-wise uncertainty map that reflects our supposed confidence in the associated computational saliency map by relating a pixel's saliency to the saliency of its direct neighbors. To estimate such uncertainties, we measure the divergence of a pixel, in a saliency map, from its local neighborhood. In addition, we propose a systematic procedure to evaluate uncertainty estimation performance by explicitly computing uncertainty ground truth as a function of a given saliency map and eye fixations of human subjects. In our experiments, we explore multiple definitions of locality and neighborhoods in spatiotemporal video signals. In addition, we examine the relationship between the parameters of our proposed algorithm and the content of the videos. The proposed algorithm is unsupervised, making it more suitable for generalization to most natural videos. Also, it is computationally efficient and flexible for customization to specific video content. Experiments using three publicly available video data sets show that the proposed algorithm outperforms state-of-the-art uncertainty estimation methods with improvement in accuracy up to 63% and offers efficiency and flexibility that make it more useful in practical situations.