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Exploring Visual Saliency of Real Objects at Different Depths

Depth cues are important aspects that influence the visual saliency of objects around us. However, the depth aspect and its quantified impact on the visual saliency has not yet been thoroughly examined in real environments. We designed and carried out an experimental study to examine the influence of the depth cues on the visual saliency of the objects at the scene. The experimental study took place with 28 participants under laboratory conditions with the objects in various depth configurations at the real scene. Visual attention data were measured by the wearable eye-tracking glasses. .. more

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Color Saliency

Color is a fundamental component of visual attention. Saliency is usually associated with color contrasts. Besides this bottom-up perspective, some recent works indicate that psychological aspects should be considered too. However, relatively little research has been done on the potential impacts of color psychology on attention. To our best knowledge, a publicly available fixation dataset specialized in color features does not exist. We, therefore, conducted a novel eye-tracking experiment with color stimuli. We studied  fixations of 15 participants to find out whether color differences can reliably model color saliency or particular colors are preferably fixated regardless of scene content, i.e. color prior. … more


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Effects of individual’s emotions on saliency and visual search

Patrik Polatsek, Miroslav Laco, Šimon Dekrét, Wanda Benesova, Martina Baránková, Bronislava Strnádelová, Jana Koróniová, Mária Gablíková


While psychological studies have confirmed a connection between emotional stimuli and visual attention, there is a lack of evidence, how much influence individual’s mood has on visual information processing of emotionally neutral stimuli. In contrast to prior studies, we explored if bottom-up low-level saliency could be affected by positive mood. We therefore induced positive or neutral emotions in 10 subjects using autobiographical memories during free-viewing, memorizing the image content and three visual search tasks. We explored differences in human gaze behavior between both emotions and relate their fixations with bottom-up saliency predicted by a traditional computational model. We observed that positive emotions produce a stronger saliency effect only during free exploration of valence-neutral stimuli. However, the opposite effect was observed during task-based analysis. We also found that tasks could be solved less efficiently when experiencing a positive mood and therefore, we suggest that it rather distracts users from a task.

download: saliency-emotions

Please cite this paper if you use the dataset:

Polatsek, P., Laco, M., Dekrét, Š., Benesova, W., Baránková, M., Strnádelová, B., Koróniová, J., & Gablíková, M. (2019)

Effects of individual’s emotions on saliency and visual search

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Saliency map

Patrik Polatsek


Saliency model predicts what attracts the attention. The results of such models are saliency maps. A saliency map is a topographic representation of saliency which refers to visually dominant locations.

The aim of the project is to implement Itti’s saliency model. It is a hierarchical biologically inspired bottom-up model based on three features: intensity, color and orientation. The resulting saliency model is created by hierarchical decomposition of the features and their combination to the single map. Attended locations are searched using Winner-take-all neuron network.

The process

First, the features are extracted from an input image.

Intensity is obtained by converting the image to grayscale.

cvtColor( input, intensity, CV_BGR2GRAY );

For color extraction the image is converted to red-green-blue-yellow color space.

R = bgr[2] - ( bgr[1] + bgr[0] ) / 2;
G = bgr[1] - ( bgr[2] + bgr[0] ) / 2;
B = bgr[0] - ( bgr[2] + bgr[1] ) / 2;
Y = ( bgr[2] + bgr[1] ) / 2 - abs( bgr[2] - bgr[1] ) / 2 - bgr[0];

Information about local orientation is extracted using Gabor filter in four angles.

Mat kernel = getGaborKernel( Size(11, 11), 2.5, degreeToRadian(theta), 2.5, 0.5 );
filter2D( input, im, -1, kernel );

The next phase consists of creation of Gaussian pyramids.

buildPyramid( channel, pyramid, levels);

Center-surround organization of receptive field of ganglion neurons is implemented as difference-of-Gaussian between finer and coarser scales of a pyramid called a feature map.

for( int i : centerScale )
for (int i : centerScale)
	pyr_c = pyramid[i];
	for (int j : surroundScale)
		Mat diff;
		resize(pyramid[i + j], pyr_s, pyr_c.size());
		absdiff(pyr_c, pyr_s, diff);

The model creates three conspicuous maps for intensity, color and orientation combining created feature maps.

The final saliency map is a mean of the conspicuous maps.

Mat saliencyMap = maps[0] / maps.size() + maps[1] / maps.size() + maps[2] / maps.size();
Basic structure of saliency model