A CBIR-based evaluation framework for visual attention models

Abstract

The computational models of visual attention, originally proposed as cognitive models of human attention, nowadays are being used as front-ends to numerous vision systems like automatic object recognition. These systems are generally evaluated against eye tracking data or manually segmented salient objects in images. We previously showed that this comparison can lead to different rankings depending on which of the two ground truths is used. These findings suggest that the saliency models ranking might be different for each application and the use of eye-tracking rankings to choose a model for a given application is not optimal. Therefore, in this paper, we propose a new saliency evaluation framework optimized for object recognition. This paper aims to answer the question: 1) Is the application-driven saliency models rankings consistent with classical ground truth like eye-tracking? 2) If not, which saliency models one should use for the precise CBIR applications?

Applications of Saliency Models

“…The central bias, which is extremely difficult to cancel or to remove, is a fundamental flaw which can significantly undermine conclusions of some studies and models' performance. Also other evaluation frameworks like the ones using segmented objects and even application-driven validation [161] will improve validation of the saliency models for real-life applications.…”