Unsupervised uncertainty analysis for video saliency detection
This paper presents a new unsupervised uncertainty estimation method for video saliency detection using spatial cues of the saliency map. The algorithm exploits the relationship between a pixel and its spatial neighbours in saliency maps to estimate the uncertainty of the saliency detected at the pixel location. Unlike supervised methods that fits uncertainty model to available training data, the proposed algorithm is based on very simple observation of the eye fixation map, which is largely influenced by human visual attention mechanisms. Thus, the proposed method is data independent. The performance of the proposed algorithm is evaluated using the challenging CRCNS video dataset and quantified using Receiver Operating Characteristics (ROC). The results are promising and could lead to robust uncertainty estimation using eye-fixation neighbourhood modeling.