A Locally Weighted Fixation Density-Based Metric for Assessing the Quality of Visual Saliency Predictions

Abstract

With the increased focus on visual attention (VA) in the last decade, a large number of computational visual saliency methods have been developed over the past few years. These models are traditionally evaluated by using performance evaluation metrics that quantify the match between predicted saliency and fixation data obtained from eye-tracking experiments on human observers. Though a considerable number of such metrics have been proposed in the literature, there are notable problems in them. In this work, we discuss shortcomings in existing metrics through illustrative examples and propose a new metric that uses local weights based on fixation density which overcomes these flaws. To compare the performance of our proposed metric at assessing the quality of saliency prediction with other existing metrics, we construct a ground-truth subjective database in which saliency maps obtained from 17 different VA models are evaluated by 16 human observers on a 5-point categorical scale in terms of their visual resemblance with corresponding ground-truth fixation density maps obtained from eyetracking data. The metrics are evaluated by correlating metric scores with the human subjective ratings.The correlation results show that the proposed evaluation metric outperforms all other popular existing metrics. Additionally, the constructed database and corresponding subjective ratings provide an insight into which of the existing metrics and future metrics are better at estimating the quality of saliency prediction and can be used as a benchmark.

High Quality Video Assessment Using Salient Features

“…However, it is difficult to assess completely depth quality in no reference fashion. In [17], a weighted fixation density based approach presented to describe quality assessment using visual saliency map to obtain high quality compression. However, this approach marginally eliminates the central bias problem but not completely using shuffling method.…”

Multi-scale Diffusion-based Salient Object Detection with Background and Objectness Seeds

“…Inspired by this visual attention mechanism used in [1], computer vision employs saliency detection to automatically find the prominent regions in a scene to reduce the computational loads in the subsequent image processing and analysis. Early efforts focus on fixation prediction [2,3] which compute a probabilistic map of a image to predict the actual human eye gaze patterns. Alternatively, salient object detection [4,5] has been effectively applied to numerous computer vision tasks such as image segmentation, object retrieval, object recognition, content-aware image resizing and so on.…”