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
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of visual attention models under 2D similarity transformations
Proceedings of the 2009 ACM symposium on Applied Computing
Towards Standardization of Evaluation Metrics and Methods for Visual Attention Models
Attention in Cognitive Systems
Esaliency (Extended Saliency): Meaningful Attention Using Stochastic Image Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
An attentional approach for perceptual grouping of spatially distributed patterns
Proceedings of the 29th DAGM conference on Pattern recognition
Goal-directed search with a top-down modulated computational attention system
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Model performance for visual attention in real 3d color scenes
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Fast and Robust Generation of Feature Maps for Region-Based Visual Attention
IEEE Transactions on Image Processing
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Standardized methods and metrics for evaluating progress of research is important in every field of science. Computational modeling of visual attention is an important area of research that aims towards machine vision according to the role model of nature. Standards for quantitative evaluation of research achievements in this field are still missing. This paper proposes some measurement methods and metrics that can be used as conventions for evaluation of artificial attention models. The proposed methodology also takes into account the needs of assessing attention under different visual behaviors and considers performance against increasing levels of visual complexity. The measurement methods for the quantities used in the evaluation metrics are designed to make autonomous machine-based evaluation feasible. Creating traces of performance by different attention models using the proposed metrics can provide an objective analysis of the state of the art in this field.