Psychophysical evaluation of individual low-level feature influences on visual attention

Berga, David; Fdez-Vidal, Xosé R.; Otazu, Xavier; Leborán, Víctor; Pardo, Xosé M.

doi:10.1016/j.visres.2018.10.006

Cited by 11 publications

(22 citation statements)

References 162 publications

(199 reference statements)

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“…Metrics used in saliency benchmarks [40] consider all fixations during viewing time with same 275 importance, making saliency hypotheses unclear of which computational procedures perform best using real image 276 datasets. Previous psychophysical studies [16,17] revealed that fixations guided by bottom-up attention are influenced 277 by the type of features that appear in the scene and their relative feature contrast. From these properties, the order of 278 fixations and the type of task can drive specific eye movement patterns and center biases, relevant in this case.…”

Section: Model Evaluation 273mentioning

confidence: 99%

“…Later studies 15 considered that maximizing information of scenes was the key factor on forming visual feature representations. To test 16 that, Bruce & Tsotsos [10] implemented a saliency model (AIM) by extracting sparse representations of image statistics 17 (using independent component analysis). These representations were found to be remarkably similar to cells in V1, 18 which follow similar spatial properties to Gabor filters [11].…”

Section: Introductionmentioning

confidence: 99%

“…28 In order to evaluate model predictions with eye movement data, certain patterns underlying human eye movement 29 behavior need to be accounted for a more detailed description and analysis of visual attention. These effects are found 30 to be dependent on context, discriminability, temporality, task and memory during scene viewing and visual search 31 [16,17]. Attention and spatial selection, therefore, is also dependent on the neuronal activations from both bottom-up 32 and top-down mechanisms.…”

Section: Introductionmentioning

confidence: 99%

“…'s work [7], the IoR can be applied to static saliency models by substracting the accumulated inhibitory map to the 256 saliency map during each gaze (̂ − { } ). ) and 230 psychophysical images (SID4VAM [17,75]). Generically, experimentation for these type 262 of datasets [76] capture fixations from about 5 to 55 subjects, looking at a monitor inside a luminance controlled 263 room while being restrained with a chin rest, located at a relative distance of 30-40 pixels per degree of visual angle 264 ( ).…”

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confidence: 99%

“…In order to evaluate performance on visual search, we utilised two metrics that account for the 296 ground truth mask of specific regions for search and the saliency map (in this context, it could be considered as a 297 "relevance" map) or predicted saccade coordinates (from locations with highest neuronal activity). The Saliency Index 298 (SI) [17,75,79] calculates the amount of energy of a saliency map inside a ROI ( ) with respect to the one outside 299 ( ), calculated as: = ( − )∕ . For the case of saccades in visual search, we considered to calculate the 300 probability of fixations inside the ROI (PFI).…”

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confidence: 99%

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Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1

Berga

Otazu

2019

Preprint

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Previous studies in that line suggested that lateral interactions of V1 cells are responsible, among other visual effects, of bottom-up visual attention (alternatively named visual salience or saliency). Our objective is to mimic these connections in the visual system with a neurodynamic network of firing-rate neurons. Early subcortical processes (i.e. retinal and thalamic) are functionally simulated. An implementation of the cortical magnification function is included to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search conditions. Results show that our model outpeforms other biologically-inpired models of saliency prediction as well as to predict visual saccade sequences during free viewing. We also show how temporal and spatial characteristics of inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) predict attention at distinct image contexts. Author summarySaliency maps are the representations of how certain visual regions attract attention in a visual scene, and these can be measured with eye movements. A myriad of computational models with artificial and biological inspiration have been able to acquire outstanding predictions of human fixations. However, most of these models have been built specifically for visual saliency, a characteristic that denies their biological plausibility for modeling distinct visual processing mechanisms or other visual processes simultaneously. In addition to saliency, our approach is also able to efficiently work for other tasks (without applying any type of training or optimization and keeping the same parametrization) such as Visual Search, Visual Discomfort [1], Brightness [2] and Color Induction [3]. By performing simulations of human physiology and its mechanisms, we propose to build a unified model that could be extended to predict and understand distinct perceptual processes in which V1 is responsible.2 discriminate redundant information [4][5][6]. In order to filter or select the information to 3 May 3, 2019 1/32 be processed in higher areas of visual processing in the brain, the HVS guides eye 4 movements towards regions that appear to be visually conspicuous or distinct in the 5 scene. This phenomena was observed during visual search tasks [7, 8], where detecting 6 early visual features (such as orientation, color or size) was done in parallel 7 (pre-attentively) or required either a serial "binding" step depending on scene context. 8 Koch & Ullman [9] came up with the hypothesis that neuronal mechanisms involved in 9 selective visual attention generate a unique "master" map from visual scenes, coined 10 with the term "saliency map". From that, Itti, Koch & Ullman [10] presented a 11 computational i...

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Section: Model Evaluation 273mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1

Berga

Otazu

2019

Preprint

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Blind image quality assessment by simulating the visual cortex

Cai

Fang

2022

Vis Comput

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Modeling bottom-up and top-down attention with a neurodynamic model of V1

Berga

Otazu

2020

Neurocomputing

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Previous studies suggested that lateral interactions of V1 cells are responsible, among other visual effects, of bottom-up visual attention (alternatively named visual salience or saliency). Our objective is to mimic these connections with a neurodynamic network of firing-rate neurons in order to predict visual attention. Early visual subcortical processes (i.e. retinal and thalamic) are functionally simulated. An implementation of the cortical magnification function is included to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search tasks. Results show that our model outpeforms other biologicallyinpired models of saliency prediction while predicting visual saccade sequences with the same model. We also show how temporal and spatial characteristics of inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) can predict attention at distinct image contexts.

show abstract

Psychophysical evaluation of individual low-level feature influences on visual attention

Cited by 11 publications

References 162 publications

Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1

Modeling Bottom-Up and Top-Down Attention with a Neurodynamic Model of V1

Blind image quality assessment by simulating the visual cortex

Modeling bottom-up and top-down attention with a neurodynamic model of V1

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