Saliency Priority of Individual Bottom-Up Attributes in Designing Visual Attention Models

2018 ◽  
Vol 10 (4) ◽  
pp. 1-18 ◽  
Author(s):  
Jila Hosseinkhani ◽  
Chris Joslin
Keyword(s):  
Visual Attention ◽  
Cognitive Bias ◽  
Visual Cues ◽  
Human Subjects ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Human Cognition ◽  
Ranking System ◽  
Bottom Up ◽  
The Individual

A key factor in designing saliency detection algorithms for videos is to understand how different visual cues affect the human perceptual and visual system. To this end, this article investigated the bottom-up features including color, texture, and motion in video sequences for a one-by-one scenario to provide a ranking system stating the most dominant circumstances for each feature. In this work, it is considered the individual features and various visual saliency attributes investigated under conditions in which the authors had no cognitive bias. Human cognition refers to a systematic pattern of perceptual and rational judgments and decision-making actions. First, this paper modeled the test data as 2D videos in a virtual environment to avoid any cognitive bias. Then, this paper performed an experiment using human subjects to determine which colors, textures, motion directions, and motion speeds attract human attention more. The proposed benchmark ranking system of salient visual attention stimuli was achieved using an eye tracking procedure.

Applications and New Research of Visual Saliency and Attention

2013 ◽  
Vol 333-335 ◽  
pp. 1171-1174
Author(s):  
Fan Hui ◽  
Ren Lu ◽  
Jin Jiang Li
Keyword(s):  
Visual Attention ◽  
Quality Evaluation ◽  
Saliency Detection ◽  
Video Quality ◽  
Visual Saliency ◽  
Attention Model ◽  
Detection Model ◽  
Detection Algorithms ◽  
New Research ◽  
Video Quality Evaluation

Drawing on the suvey of visual attention degree and its significance in psychology and physiology , in recent years, researchers have proposed a lot of visual attention model and algorithms, such as Itti model and many saliency detection algorithms. And in recent years, the researchers applied the visual attention of this technology in a lot of directions, such as a significant regional shifts and visual tracing detection model based on network loss, for video quality evaluation. This paper summarizes the various algorithms and its application of visual attention and its significance.

scholarly journals A Survey on Approaches for Saliency Detection with Visual Attention

2018 ◽  
Vol 232 ◽  
pp. 02007
Author(s):  
Qi Zhang
Keyword(s):  
Deep Learning ◽  
Visual Attention ◽  
Visual Cues ◽  
Saliency Detection ◽  
Saliency Map ◽  
Local Context ◽  
Natural Scenes ◽  
Benchmark Datasets ◽  
The Difference

Most existing approaches for detecting salient areas in natural scenes are based on the saliency contrast within the local context of image. Nowadays, a few approaches not only consider the difference between the foreground objects and the surrounding background areas, but also consider the saliency objects as the candidates for the center of attention from the human’s perspective. This article provides a survey of saliency detection with visual attention, which exploit visual cues of foreground salient areas, visual attention based on saliency map, and deep learning based saliency detection. The published works are explained and descripted in detail, and some related key benchmark datasets are briefly presented. In this article, all documents are published from 2013 to 2018, giving an overview of the progress of the field of saliency detection.

Visual saliency and potential field data enhancements: Where is your attention drawn?

2014 ◽  
Vol 2 (4) ◽  
pp. SJ9-SJ21 ◽  
Author(s):  
Yathunanthan Sivarajah ◽  
Eun-Jung Holden ◽  
Roberto Togneri ◽  
Michael Dentith ◽  
Mark Lindsay
Keyword(s):  
Visual Attention ◽  
Image Enhancement ◽  
Feature Detection ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Data Interpretation ◽  
Detection Algorithm ◽  
Magnetic Data ◽  
Data Set ◽  
Potential Field Data

Interpretation of gravity and magnetic data for exploration applications may be based on pattern recognition in which geophysical signatures of geologic features associated with localized characteristics are sought within data. A crucial control on what comprises noticeable and comparable characteristics in a data set is how images displaying those data are enhanced. Interpreters are provided with various image enhancement and display tools to assist their interpretation, although the effectiveness of these tools to improve geologic feature detection is difficult to measure. We addressed this challenge by analyzing how image enhancement methods impact the interpreter’s visual attention when interpreting the data because features that are more salient to the human visual system are more likely to be noticed. We used geologic target-spotting exercises within images generated from magnetic data to assess commonly used magnetic data visualization methods for their visual saliency. Our aim was achieved in two stages. In the first stage, we identified a suitable saliency detection algorithm that can computationally predict visual attention of magnetic data interpreters. The computer vision community has developed various image saliency detection algorithms, and we assessed which algorithm best matches the interpreter’s data observation patterns for magnetic target-spotting exercises. In the second stage, we applied this saliency detection algorithm to understand potential visual biases for commonly used magnetic data enhancement methods. We developed a guide to choosing image enhancement methods, based on saliency maps that minimize unintended visual biases in magnetic data interpretation, and some recommendations for identifying exploration targets in different types of magnetic data.

Visual saliency detection via integrating bottom-up and top-down information

Optik ◽  
2019 ◽  
Vol 178 ◽  
pp. 1195-1207 ◽  
Author(s):  
Zahra Sadat Shariatmadar ◽  
Karim Faez
Keyword(s):  
Saliency Detection ◽  
Visual Saliency ◽  
Top Down ◽  
Bottom Up ◽  
Visual Saliency Detection

scholarly journals Leveraging colour-based pseudo-labels to supervise saliency detection in hyperspectral image datasets

2021 ◽  
Author(s):  
Annalisa Appice ◽  
Angelo Cannarile ◽  
Antonella Falini ◽  
Donato Malerba ◽  
Francesca Mazzia ◽  
...  
Keyword(s):  
Visual Attention ◽  
Hyperspectral Image ◽  
Saliency Detection ◽  
Hyperspectral Images ◽  
Spectral Information ◽  
Imaging Analysis ◽  
Analysis Task ◽  
Visual Attention Mechanism ◽  
Land Use Monitoring ◽  
Research Resources

AbstractSaliency detection mimics the natural visual attention mechanism that identifies an imagery region to be salient when it attracts visual attention more than the background. This image analysis task covers many important applications in several fields such as military science, ocean research, resources exploration, disaster and land-use monitoring tasks. Despite hundreds of models have been proposed for saliency detection in colour images, there is still a large room for improving saliency detection performances in hyperspectral imaging analysis. In the present study, an ensemble learning methodology for saliency detection in hyperspectral imagery datasets is presented. It enhances saliency assignments yielded through a robust colour-based technique with new saliency information extracted by taking advantage of the abundance of spectral information on multiple hyperspectral images. The experiments performed with the proposed methodology provide encouraging results, also compared to several competitors.

scholarly journals EXPRESS: Two Variations and One Similarity in Memory Functions Deployed by Mice and Humans to Support Foraging

2021 ◽  
pp. 174702182110105
Author(s):  
Spencer Talbot ◽  
Todor Gerdjikov ◽  
Carlo De Lillo
Keyword(s):  
Working Memory ◽  
Brain Function ◽  
Visual Cues ◽  
Brain Structures ◽  
Human Cognition ◽  
Foraging Efficiency ◽  
Long Term Memory ◽  
Memory Functions ◽  
Term Memory

Assessing variations in cognitive function between humans and animals is vital for understanding the idiosyncrasies of human cognition and for refining animal models of human brain function and disease. We determined memory functions deployed by mice and humans to support foraging with a search task acting as a test battery. Mice searched for food from the top of poles within an open-arena. Poles were divided into groups based on visual cues and baited according to different schedules. White and black poles were baited in alternate trials. Striped poles were never baited. The requirement of the task was to find all baits in each trial. Mice’s foraging efficiency, defined as the number of poles visited before all baits were retrieved, improved with practice. Mice learnt to avoid visiting un-baited poles across trials (Long-term memory) and revisits to poles within each trial (Working memory). Humans tested with a virtual-reality version of the task outperformed mice in foraging efficiency, working memory and exploitation of the temporal pattern of rewards across trials. Moreover, humans, but not mice, reduced the number of possible movement sequences used to search the set of poles. For these measures interspecies differences were maintained throughout three weeks of testing. By contrast, long-term-memory for never-rewarded poles was similar in mice and humans after the first week of testing. These results indicate that human cognitive functions relying upon archaic brain structures may be adequately modelled in mice. Conversely, modelling in mice fluid skills likely to have developed specifically in primates, requires caution.

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