2 research outputs found
The Effect of Distortions on the Prediction of Visual Attention
Existing saliency models have been designed and evaluated for predicting the
saliency in distortion-free images. However, in practice, the image quality is
affected by a host of factors at several stages of the image processing
pipeline such as acquisition, compression and transmission. Several studies
have explored the effect of distortion on human visual attention; however, none
of them have considered the performance of visual saliency models in the
presence of distortion. Furthermore, given that one potential application of
visual saliency prediction is to aid pooling of objective visual quality
metrics, it is important to compare the performance of existing saliency models
on distorted images. In this paper, we evaluate several state-of-the-art visual
attention models over different databases consisting of distorted images with
various types of distortions such as blur, noise and compression with varying
levels of distortion severity. This paper also introduces new improved
performance evaluation metrics that are shown to overcome shortcomings in
existing performance metrics. We find that the performance of most models
improves with moderate and high levels of distortions as compared to the near
distortion-free case. In addition, model performance is also found to decrease
with an increase in image complexity.Comment: 14 pages, 2 column, 14 figure
A Locally Weighted Fixation Density-Based Metric for Assessing the Quality of Visual Saliency Predictions
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 eye-tracking 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