Preliminary Study Towards a Fuzzy Model for Visual Attention

Attention, in particular visual attention, has been a subject of studies in various disciplines, including cognitive science, experimental psychology, and computer vision. In cognitive science and experimental psychology the objective is to develop theories that can explain the attention phenomenon of cognition. In computer vision, the objective is to inform image understanding systems by hypotheses on the human visual attention. There is, however, very little influence of studies across these two disciplines. In a departure from this state of affairs, this study seeks to develop an algorithmic approach to visual attention as part of an image understanding system, by starting with a theory of visual attention put forward in experimental psychology. In the process, it will become useful to revise some of the concepts of this theory, in particular by adopting fuzzy set based representations and the necessary calculus for them.

[1]  Didier Dubois,et al.  A review of fuzzy set aggregation connectives , 1985, Inf. Sci..

[2]  R. Yager.  Connectives and quantifiers in fuzzy sets , 1991 .

[3]  D. Ralescu.  Cardinality, quantifiers, and the aggregation of fuzzy criteria , 1995 .

[4]  Li Xu,et al.  Hierarchical Saliency Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Isabelle Bloch.  Information combination operators for data fusion: a comparative review with classification , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[6]  Glyn W. Humphreys,et al.  Feature Confirmation in Object Perception: Feature Integration Theory 26 Years on from the Treisman Bartlett Lecture , 2016, Quarterly journal of experimental psychology.

[7]  A. Treisman.  Features and Objects: The Fourteenth Bartlett Memorial Lecture , 1988, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[8]  Anca L. Ralescu,et al.  A note on rule representation in expert systems , 1986, Inf. Sci..

[9]  Fergus I. M. Craik,et al.  The Psychological Reality of Levels of Processing , 2014 .

[10]  Isabelle Bloch,et al.  Sequential model-based segmentation and recognition of image structures driven by visual features and spatial relations , 2012, Comput. Vis. Image Underst..

[11]  C. Bundesen,et al.  A neural theory of visual attention: bridging cognition and neurophysiology. , 2005, Psychological review.

[12]  Huchuan Lu,et al.  Saliency Detection via Graph-Based Manifold Ranking , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Jean-Michel Morel,et al.  Computational gestalts and perception thresholds , 2003, Journal of Physiology-Paris.

[14]  C. Bundesen,et al.  Recent developments in a computational theory of visual attention (TVA) , 2015, Vision Research.

[15]  C. Bundesen.  A theory of visual attention. , 1990, Psychological review.

[16]  Ali Borji,et al.  State-of-the-Art in Visual Attention Modeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Nuno Vasconcelos,et al.  The discriminant center-surround hypothesis for bottom-up saliency , 2007, NIPS.

[18]  D. Kahneman.  Attention and Effort , 1973 .

[19]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.