Contextual Modulation of Target Saliency

The most popular algorithms for object detection require the use of exhaustive spatial and scale search procedures. In such approaches, an object is defined by means of local features. In this paper we show that including contextual information in object detection procedures provides an efficient way of cutting down the need for exhaustive search. We present results with real images showing that the proposed scheme is able to accurately predict likely object classes, locations and sizes.

[1]  Alex Pentland,et al.  Probabilistic Visual Learning for Object Representation , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Sayan Mukherjee,et al.  Feature reduction and hierarchy of classifiers for fast object detection in video images , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[3]  Serge J. Belongie,et al.  Region-based image querying , 1997, 1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries.

[4]  Neil Gershenfeld,et al.  The nature of mathematical modeling , 1998 .

[5]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[6]  Rosalind W. Picard,et al.  Texture orientation for sorting photos "at a glance" , 1994, Proceedings of 12th International Conference on Pattern Recognition.

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

[8]  Thomas M. Strat,et al.  Context-Based Vision: Recognizing Objects Using Information from Both 2D and 3D Imagery , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Martin Szummer,et al.  Indoor-outdoor image classification , 1998, Proceedings 1998 IEEE International Workshop on Content-Based Access of Image and Video Database.

[10]  I. Biederman,et al.  Scene perception: Detecting and judging objects undergoing relational violations , 1982, Cognitive Psychology.

[11]  J. Wolfe,et al.  Guided Search 2.0 A revised model of visual search , 1994, Psychonomic bulletin & review.

[12]  Rajesh P. N. Rao,et al.  Modeling Saccadic Targeting in Visual Search , 1995, NIPS.

[13]  Antonio Torralba,et al.  Statistical Context Priming for Object Detection , 2001, ICCV.