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Proceedings Paper

Measuring saliency in images: which experimental parameters for the assessment of image quality?
Author(s): Clement Fredembach; Geoff Woolfe; Jue Wang
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Paper Abstract

Predicting which areas of an image are perceptually salient or attended to has become an essential pre-requisite of many computer vision applications. Because observers are notoriously unreliable in remembering where they look a posteriori, and because asking where they look while observing the image necessarily in uences the results, ground truth about saliency and visual attention has to be obtained by gaze tracking methods. From the early work of Buswell and Yarbus to the most recent forays in computer vision there has been, perhaps unfortunately, little agreement on standardisation of eye tracking protocols for measuring visual attention. As the number of parameters involved in experimental methodology can be large, their individual in uence on the nal results is not well understood. Consequently, the performance of saliency algorithms, when assessed by correlation techniques, varies greatly across the literature. In this paper, we concern ourselves with the problem of image quality. Specically: where people look when judging images. We show that in this case, the performance gap between existing saliency prediction algorithms and experimental results is signicantly larger than otherwise reported. To understand this discrepancy, we rst devise an experimental protocol that is adapted to the task of measuring image quality. In a second step, we compare our experimental parameters with the ones of existing methods and show that a lot of the variability can directly be ascribed to these dierences in experimental methodology and choice of variables. In particular, the choice of a task, e.g., judging image quality vs. free viewing, has a great impact on measured saliency maps, suggesting that even for a mildly cognitive task, ground truth obtained by free viewing does not adapt well. Careful analysis of the prior art also reveals that systematic bias can occur depending on instrumental calibration and the choice of test images. We conclude this work by proposing a set of parameters, tasks and images that can be used to compare the various saliency prediction methods in a manner that is meaningful for image quality assessment.

Paper Details

Date Published: 24 January 2012
PDF: 10 pages
Proc. SPIE 8293, Image Quality and System Performance IX, 82930N (24 January 2012); doi: 10.1117/12.913668
Show Author Affiliations
Clement Fredembach, Canon Information Systems Research Australia Pty. Ltd. (Australia)
Geoff Woolfe, Canon Information Systems Research Australia Pty. Ltd. (Australia)
Jue Wang, Canon Information Systems Research Australia Pty. Ltd. (Australia)


Published in SPIE Proceedings Vol. 8293:
Image Quality and System Performance IX
Frans Gaykema; Peter D. Burns, Editor(s)

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