A Novel Method of Visual Attention for Targets Detection

Inspired by the research of human visual system in neuroanatomy and psychology, the paper proposes a two-way collaborative visual attention model for target detection.In this new method , bottom-up attention information cooperates with top-down attention information to detect a target rapidly and accuractly. Firstly,the statistical prior knowledge of target and background is applied to optimize bottom-up attention information in different feature space and scale space.Secondly, after the SNR of salience difference between target and interference is computed ,the bottom-up gain factor is obtained.Thirdly, the gain factor is applied to adjust bottom up attention information extraction and then to maximize the salience contrast of target and background.Finally, target is detected by adjusted saliency.Experimental results shows that the proposed model in this paper can improve the real-time capability and reliability of target detection.

[1]  Albert Ali Salah,et al.  A Selective Attention-Based Method for Visual Pattern Recognition with Application to Handwritten Digit Recognition and Face Recognition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Fred Stentiford.  Attention-based image similarity measure with application to content-based information retrieval , 2003, IS&T/SPIE Electronic Imaging.

[3]  Jang-Kyoo Shin,et al.  Biologically Inspired Saliency Map Model for Bottom-up Visual Attention , 2002, Biologically Motivated Computer Vision.

[4]  C. Koch,et al.  A saliency-based search mechanism for overt and covert shifts of visual attention , 2000, Vision Research.

[5]  C. Chabris,et al.  Gorillas in Our Midst: Sustained Inattentional Blindness for Dynamic Events , 1999, Perception.

[6]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[7]  F. Stentiford.  An attention based similarity measure with application to content-based information retrieval , 2002 .

[8]  Heinrich H. Bülthoff,et al.  Biologically motivated computer vision : Second International Workshop, BMCV 2002, Tübingen, Germany, November 22-24, 2002 : proceedings , 2002 .

[9]  Christopher M. Brown,et al.  Selective Attention as Sequential Behavior: Modeling Eye Movements with an Augmented Hidden Markov Model , 1990 .

[10]  J. Duncan,et al.  Visual search and stimulus similarity. , 1989, Psychological review.

[11]  I. Ohzawa,et al.  Receptive-field dynamics in the central visual pathways , 1995, Trends in Neurosciences.

[12]  Patrick Le Callet,et al.  A coherent computational approach to model bottom-up visual attention , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Laurent Itti,et al.  A Goal Oriented Attention Guidance Model , 2002, Biologically Motivated Computer Vision.