The ability to automatically detect visually interesting regions in images has many practical applications, especially in the design of active machine vision and automatic visual surveillance systems. Analysis of the statistics of image features at observers' gaze can provide insights into the mechanisms of fixation selection in humans. Using a foveated analysis framework, we studied the statistics of four low-level local image features: luminance, contrast, and bandpass outputs of both luminance and contrast, and discovered that image patches around human fixations had, on average, higher values of each of these features than image patches selected at random. Contrast-bandpass showed the greatest difference between human and random fixations, followed by luminance-bandpass, RMS contrast, and luminance. Using these measurements, we present a new algorithm that selects image regions as likely candidates for fixation. These regions are shown to correlate well with fixations recorded from human observers.

Visual search experiments have usually involved the detection of a salient target in the presence of distracters against a blank background. In such high signal-to-noise scenarios, observers have been shown to use visual cues such as color, size, and shape of the target to program their saccades during visual search. The degree to which these features affect search performance is usually measured using reaction times and detection accuracy. We asked whether human observers are able to use target features to succeed in visual search tasks in stimuli with very low signal-to-noise ratios. Using the classification image analysis technique, we investigated whether observers used structural cues to direct their fixations as they searched for simple geometric targets embedded at very low signal-to-noise ratios in noise stimuli that had the spectral characteristics of natural images. By analyzing properties of the noise stimulus at observers' fixations, we were able to reveal idiosyncratic, target-dependent features used by observers in our visual search task. We demonstrate that even in very noisy displays, observers do not search randomly, but in many cases they deploy their fixations to regions in the stimulus that resemble some aspect of the target in their local image features.

Abstract-DOVES, a database of visual eye movements, is a set of eye movements collected from 29 human observers as they viewed 101 natural calibrated images. Recorded using a highprecision dual-Purkinje eye tracker, the database consists of around 30 000 fixation points, and is believed to be the first large-scale database of eye movements to be made available to the vision research community. The database, along with MATLAB functions for its use, may be downloaded freely from http://live.ece.utexas.edu/research/doves, and used without restriction for educational and research purposes, providing that this paper is cited in any published work. This paper documents the acquisition procedure, summarises common eye movement statistics, and highlights numerous research topics for which DOVES may be used.

Analysis of the statistics of image features at observers' gaze can provide insights into the mechanisms of fixation selection in humans. Using a foveated analysis framework, in which image patches were analyzed at the resolution corresponding to their eccentricity from the prior fixation, we studied the statistics of four low-level local image features: luminance, RMS contrast, and bandpass outputs of both luminance and contrast, and discovered that the image patches around human fixations had, on average, higher values of each of these features at all eccentricities than the image patches selected at random. Bandpass contrast showed the greatest difference between human and random fixations, followed by bandpass luminance, RMS contrast, and luminance. An eccentricity-based analysis showed that shorter saccades were more likely to land on patches with higher values of these features. Compared to a full-resolution analysis, foveation produced an increased difference between human and random patch ensembles for contrast and its higher-order statistics.