The cortex transform: rapid computation of simulated neural images
Computer Vision, Graphics, and Image Processing
Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
A neural model of contour integration in the primary visual cortex
Neural Computation
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spontaneous eye movements during visual imagery reflect the content of the visual scene
Journal of Cognitive Neuroscience
Region-based visual attention analysis with its application in image browsing on small displays
Proceedings of the 15th international conference on Multimedia
Perceptual image representation
Journal on Image and Video Processing
Expert Systems with Applications: An International Journal
ACM SIGGRAPH 2008 papers
An efficient algorithm for attention-driven image interpretation from segments
Pattern Recognition
Optimal Cue Combination for Saliency Computation: A Comparison with Human Vision
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Color in image and video processing: most recent trends and future research directions
Journal on Image and Video Processing - Color in Image and Video Processing
A generic virtual content insertion system based on visual attention analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Color Saliency and Inhibition Using Static and Dynamic Scenes in Region Based Visual Attention
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Real-world vision: Selective perception and task
ACM Transactions on Applied Perception (TAP)
International Journal of Human-Computer Studies
Modelling Spatio-Temporal Saliency to Predict Gaze Direction for Short Videos
International Journal of Computer Vision
Modeling Bottom-Up Visual Attention for Color Images
IEICE - Transactions on Information and Systems
Bio-inspired visual attention in agile sensing for target detection
International Journal of Sensor Networks
Towards Standardization of Evaluation Metrics and Methods for Visual Attention Models
Attention in Cognitive Systems
Visual attention analysis by pseudo gravitational field
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Salient region detection by modeling distributions of color and orientation
IEEE Transactions on Multimedia
An Approach for Preparing Groundtruth Data and Evaluating Visual Saliency Models
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Axiomatic approach to computational attention
Pattern Recognition
Salient region extraction based on intensity mapping for image retrieval
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
An attentional approach for perceptual grouping of spatially distributed patterns
Proceedings of the 29th DAGM conference on Pattern recognition
Using Human Visual System modeling for bio-inspired low level image processing
Computer Vision and Image Understanding
Early clustering approach towards modeling of bottom-up visual attention
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
What we see is most likely to be what matters: visual attention and applications
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
On the role of context in probabilistic models of visual saliency
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Main subject detection VIA adaptive feature selection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Image Processing
A biologically inspired object-based visual attention model
Artificial Intelligence Review
Overt visual attention for free-viewing and quality assessment tasks
Image Communication
what is the chance of happening: a new way to predict where people look
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Relevance of a feed-forward model of visual attention for goal-oriented and free-viewing tasks
IEEE Transactions on Image Processing
Random walks on graphs for salient object detection in images
IEEE Transactions on Image Processing
Automatic prediction of perceptual quality of multimedia signals--a survey
Multimedia Tools and Applications
Bottom-up saliency detection model based on amplitude spectrum
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
3D saliency for abnormal motion selection: the role of the depth map
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
Comparative visibility analysis of advertisement images
Image Communication
Prediction of the inter-observer visual congruency (IOVC) and application to image ranking
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Linear vs. nonlinear feature combination for saliency computation: a comparison with human vision
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Saliency from hierarchical adaptation through decorrelation and variance normalization
Image and Vision Computing
Global salient information maximization for saliency detection
Image Communication
Modulating Shape Features by Color Attention for Object Recognition
International Journal of Computer Vision
A dynamic saliency attention model based on local complexity
Digital Signal Processing
Non-local spatial redundancy reduction for bottom-up saliency estimation
Journal of Visual Communication and Image Representation
Towards standardization of metrics for evaluation of artificial visual attention
Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop
Proceedings of the 20th ACM international conference on Multimedia
Two-layer average-to-peak ratio based saliency detection
Image Communication
Saliency maps of high dynamic range images
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
How to measure the relevance of a retargeting approach?
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
Learning saliency-based visual attention: A review
Signal Processing
Journal of Visual Communication and Image Representation
Saliency-Guided consistent color harmonization
CCIW'13 Proceedings of the 4th international conference on Computational Color Imaging
Visual saliency detection using information divergence
Pattern Recognition
An edge detection with automatic scale selection approach to improve coherent visual attention model
Pattern Recognition Letters
Stochastic bottom-up fixation prediction and saccade generation
Image and Vision Computing
Spatiotemporal saliency detection and salient region determination for H.264 videos
Journal of Visual Communication and Image Representation
Discriminative two-level feature selection for realistic human action recognition
Journal of Visual Communication and Image Representation
Selection of a best metric and evaluation of bottom-up visual saliency models
Image and Vision Computing
Salient object detection based on regions
Multimedia Tools and Applications
Retina enhanced SURF descriptors for spatio-temporal concept detection
Multimedia Tools and Applications
Visual Saliency with Statistical Priors
International Journal of Computer Vision
Hi-index | 0.15 |
Visual attention is a mechanism which filters out redundant visual information and detects the most relevant parts of our visual field. Automatic determination of the most visually relevant areas would be useful in many applications such as image and video coding, watermarking, video browsing, and quality assessment. Many research groups are currently investigating computational modeling of the visual attention system. The first published computational models have been based on some basic and well-understood Human Visual System (HVS) properties. These models feature a single perceptual layer that simulates only one aspect of the visual system. More recent models integrate complex features of the HVS and simulate hierarchical perceptual representation of the visual input. The bottom-up mechanism is the most occurring feature found in modern models. This mechanism refers to involuntary attention (i.e., salient spatial visual features that effortlessly or involuntary attract our attention). This paper presents a coherent computational approach to the modeling of the bottom-up visual attention. This model is mainly based on the current understanding of the HVS behavior. Contrast sensitivity functions, perceptual decomposition, visual masking, and center-surround interactions are some of the features implemented in this model. The performances of this algorithm are assessed by using natural images and experimental measurements from an eye-tracking system. Two adequate well-known metrics (correlation coefficient and Kullbacl-Leibler divergence) are used to validate this model. A further metric is also defined. The results from this model are finally compared to those from a reference bottom-up model.