A Comparison of Feature Detectors with Passive and Task-Based Visual Saliency

Harding, Patrick; Robertson, Neil

doi:10.1007/978-3-642-02230-2_73

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…The intention was to investigate to what extent these detectors are qualified to predict bottom-up as well as top-down gaze targets. While our results (the key point localization step of the SIFT algorithm performed best) correspond to the findings in (Harding and Robertson, 2009) and (Rajashekar et al, 2007) we identified a strong interplay of top-down cues (i.e. the retrieval task was too simple w.r.t.…”

Section: Related Worksupporting

confidence: 84%

ROTATIONAL INVARIANCE AT FIXATION POINTS - Experiments using Human Gaze Data

Steffen

Hentschel

Alyosef

et al. 2012

Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

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An important aspect in machine vision concerns the extraction of meaningful patterns at salient image regions. Invariance w.r.t. affine transformations has usually been claimed to be a crucial attribute of these regions. While continuing research on the human visual cortex has suggested the correctness of these assumptions at least in later stages of vision, only lately the availability of accurate and cheap eye tracking devices has offered the possibility to provide empirical evidence to these claims. We present an experimental setting that is qualified to analyse various assumptions on human gaze target properties. The proposed setting aims at reducing high-level influence on the fixation process as much as possible. As a proof of concept we present results for the assumption human fixation targeting is rotational invariant. Even though high-level aspects could not be completely suppressed, we were able to detect and analyse this relation in the gaze data. It was found that there is a significant correlation between fixated regions within stimuli over different orientations.

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Section: Related Worksupporting

confidence: 84%

ROTATIONAL INVARIANCE AT FIXATION POINTS - Experiments using Human Gaze Data

Steffen

Hentschel

Alyosef

et al. 2012

Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

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“…The contributions presented in [Harding, P. et al 2009;Chen, H. et al 2011] developed the concept of task driven saliency maps. The authors compared the performance of difference saliency maps by using thresholding on saliency levels and then check whether higher values correspond to positions of the object of interest.…”

Section: Related Workmentioning

confidence: 99%

Experts vs. novices

Bernal

Sánchez

Vilariño

et al. 2014

Proceedings of the Symposium on Eye Tracking Research and Applications

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We present in this paper a novel study aiming at identifying the differences in visual search patterns between physicians of diverse levels of expertise during the screening of colonoscopy videos. Physicians were clustered into two groups -experts and novices-according to the number of procedures performed, and fixations were captured by an eye-tracker device during the task of polyp search in different video sequences. These fixations were integrated into heat maps, one for each cluster. The obtained maps were validated over a ground truth consisting of a mask of the polyp, and the comparison between experts and novices was performed by using metrics such as reaction time, dwelling time and energy concentration ratio. Experimental results show a statistically significant difference between experts and novices, and the obtained maps show to be a useful tool for the characterisation of the behaviour of each group.

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“…The first study where local feature detectors were compared with saliency information was published by Harding and Robertson [8]. These authors compared six interest point detectors with two bottom-up saliency models and eye-tracking data.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Local Feature Detectors in Salient Region Detection

2013

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Abstract. In this work, we study local feature extraction methods and evaluate their performance in detecting local features from the salient regions of images. In order to measure the detectors' performance, we compared the detected regions to gaze fixations obtained from the eye movement recordings of human participants viewing two types of images: natural images (photographs) and abstract/surreal images. The results indicate that all of the six evaluated local feature detectors perform clearly above chance level. The Hessian-Affine detector performs the best and almost reaches the performance level of state-of-the-art saliency detection methods.

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A Comparison of Feature Detectors with Passive and Task-Based Visual Saliency

Cited by 11 publications

References 16 publications

ROTATIONAL INVARIANCE AT FIXATION POINTS - Experiments using Human Gaze Data

ROTATIONAL INVARIANCE AT FIXATION POINTS - Experiments using Human Gaze Data

Experts vs. novices

Evaluating Local Feature Detectors in Salient Region Detection

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