Since the early 2000s, computational visual saliency has been a very active
research area. Each year, more and more new models are published in the main
computer vision conferences...
Nowadays, one of the big challenges is to find a
way to fairly evaluate all of these models. In this paper, a new framework is
proposed to assess models of visual saliency. This evaluation is divided into
three experiments leading to the proposition of a new evaluation framework. Each experiment is based on a basic question: 1) there are two ground truths
for saliency evaluation: what are the differences between eye fixations and
manually segmented salient regions?, 2) the properties of the salient regions:
for example, do large, medium and small salient regions present different
difficulties for saliency models? and 3) the metrics used to assess saliency
models: what advantages would there be to mix them with PCA? Statistical
analysis is used here to answer each of these three questions.