• Corpus ID: 12468274

Which Colors Best Catch Your Eyes: a Subjective Study of Color Saliency

@inproceedings{Gelasca2005WhichCB,
  title={Which Colors Best Catch Your Eyes: a Subjective Study of Color Saliency},
  author={Elisa Drelie Gelasca and Danijel Toma{\vs}i{\'c} and Touradj Ebrahimi},
  year={2005}
}
To determine Regions of Interest (ROI) in a scene, percep-tual saliency of regions has to be measured. When scenes are viewed with the same context and motivation, these ROIs are often highly correlated among different people. As a result, it is possible to develop a computational model of visual attention that can analyze a scene and accurately esti-mate the location of viewers ROIs. Color saliency is inves-tigated in this paper. In particular, a subjective experiment has been carried out to… 
infoscience.epfl.ch
45 Citations
Background Citations
18
Methods Citations
3
Results Citations
1

Figures and Tables from this paper

45 Citations

Saliency Priority of Individual Bottom-Up Attributes in Designing Visual Attention Models
TLDR
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 to determine which colors, textures, motion directions, andmotion speeds attract human attention more.
Influence of color on visual saliency in short videos
TLDR
This work proposes to investigate the issue of visual saliency of videos from the perspective of color information that has been clearly identified as an important salient property in static images.
Significance of Bottom-Up Attributes in Video Saliency Detection without Cognitive Bias
  • Jila Hosseinkhani, C. Joslin
  • Computer Science
    2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)
  • 2018
TLDR
This work investigated the bottom-up features of salient visual attention stimuli including color, texture, and motion in video sequences for both one-by-one and combined scenarios to provide a ranking system stating the most dominant circumstances for each feature individually and in combination with other features as well.
A segment-based image saliency detection
TLDR
This paper presents a novel method of visual saliency detection that operates with segments rather than with separate pixels and shows that the method is successful in highlighting the mass of the object of interest.
Modelling of Human Visual Attention
In recent decades, visual attention modelling became a prominent research area. In order to simulate human attention, a computational model has to incorporate various stimulus-driven and…
Visual Saliency Detection and its Application to Image Retrieval
TLDR
The development of an accurate visual importance estimation method may bring a useful tool for image processing domain and that is the main goal for this work.
Using Eye Tracking to Investigate Saliency of Color Attributes for Digital Interfaces with Icon Matrix Arrangements Analysis on the effects of hue, tone and color combination on visual attention and saliency
The purpose of this study is to determine which colors are most eye-catching in displays that employ icon matrices, and thereby provide empirical grounds for strengthening the visual information…
Dynamic Saliency Model Inspired by Middle Temporal Visual Area: A Spatio-Temporal Perspective
TLDR
It is demonstrated that a combined model of bio-inspired spatio-temporal features, high-level and low-level features outperform listed models in predicting human fixation on dynamic visual input.
A simple and effective saliency detection approach
TLDR
The proposed method is computationally very simple, but it achieves a very good performance in the comparison experiments with six other saliency detection methods in both identifying salient pixels and segmenting salient regions.
Visual Attention in Active Vision Systems : Attending, Classifying and Manipulating Objects
TLDR
The dynamic aspects of the attentional mechanism proved to allow for a temporally varying trade-off between top-down and bottom-up influences depending on changes in the environment of the robot, as opposed to many other attentional (saliency) models that exclusively deal with static images.
...
...

References

SHOWING 1-10 OF 11 REFERENCES
Automatic detection of regions of interest in complex video sequences
TLDR
A novel model of visual attention designed to provide an accurate and robust prediction of a viewer's locus of attention across a wide range of typical video content is described.
Perceptual vision models for picture quality assessment and compression applications
TLDR
A new picture quality metric is proposed which models both early vision and higher level attention processes and it is shown that the inclusion of the attention model in the quality metric provides a statistically significant increase in the correlation between the model's prediction and subjective opinion.
Automatic location and tracking of the facial region in color video sequences
Annoyance of spatio-temporal artifacts in segmentation quality assessment [video sequences]
TLDR
Annoyance of segmentation artifacts are found to be tied up with their intrinsic characteristics but only weakly related to the video content, identifying the characteristics that should be taken into account in the design of a perceptually driven objective metric.
Towards Perceptually Driven Segmentation Evaluation Metrics
TLDR
A formal protocol for subjective tests for segmentation quality assessment is presented and the results show how an objective segmentation evaluation metric can be defined as a function of various error types.
Estimation of video object's relevance
With the advent of the MPEG-4 and MPEG-7 standards [2, 3], a further impulse to the representation and description of multimedia information has been given. In particular, object-based coding and…
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
TLDR
A fast and flexible algorithm for computing watersheds in digital gray-scale images is introduced, based on an immersion process analogy, which is reported to be faster than any other watershed algorithm.
Vision Science: Photons to Phenomenology by Stephen E. Palmer
  • H. Barlow
  • Physics
    Trends in Cognitive Sciences
  • 2000
Pyramidal implementation of the lucas kanade feature tracker
TLDR
It is essential to define the notion of similarity in a 2D neighborhood sense and the image velocity d is defined as being the vector that minimizes the residual function defined as follows.
Le Pouvoir de la Couleur
  • Les Editions de l’Homme
  • 1998
...
...