A comparison of feature detectors with passive and task-based visual saliency

Abstract

This paper investigates the coincidence between six interest point detection methods (SIFT, MSER, Harris-Laplace, SURF, FAST & Kadir-Brady Saliency) with two robust "bottom-up" models of visual saliency (Itti and Harel) as well as "task" salient surfaces derived from observer eye-tracking data. Comprehensive statistics for all detectors vs. saliency models are presented in the presence and absence of a visual search task. It is found that SURF interest-points generate the highest coincidence with saliency and the overlap is superior by 15% for the SURF detector compared to other features. The overlap of image features with task saliency is found to be also distributed towards the salient regions. However the introduction of a specific search task creates high ambiguity in knowing how attention is shifted. It is found that the Kadir-Brady interest point is more resilient to this shift but is the least coincident overall. © 2009 Springer Berlin Heidelberg.