Contrast-based image attention analysis by using fuzzy growing

Visual attention analysis provides an alternative methodology to semantic image understanding in many applications such as adaptive content delivery and region-based image retrieval. In this paper, we propose a feasible and fast approach to attention area detection in images based on contrast analysis. The main contributions are threefold: 1) a new saliency map generation method based on local contrast analysis is proposed; 2) by simulating human perception, a fuzzy growing method is used to extract attended areas or objects from the saliency map; and 3) a practicable framework for image attention analysis is presented, which provides three-level attention analysis, i.e., attended view, attended areas and attended points. This framework facilitates visual analysis tools or vision systems to automatically extract attentions from images in a manner like human perception. User study results indicate that the proposed approach is effective and practicable.

[1]  D. Spalding.  The Principles of Psychology , 1873, Nature.

[2]  O. Reiser,et al.  Principles Of Gestalt Psychology , 1936 .

[3]  D. Broadbent.  Perception and communication , 1958 .

[4]  J. Deutsch,et al.  Militarized youths in western Côte d’Ivoire: local processes of mobilization, demobilization, and related humanitarian interventions (2002-2007) , 2011 .

[5]  L. Zadeh.  Probability measures of Fuzzy events , 1968 .

[6]  L. Zusne.  Visual perception of form , 1970 .

[7]  S Ullman,et al.  Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.

[8]  Anne Treisman,et al.  Features and objects in visual processing , 1986 .

[9]  H. Bourgeois,et al.  [Contrast sensitivity]. , 1987, L'Annee therapeutique et clinique en ophtalmologie.

[10]  A Treisman,et al.  Feature analysis in early vision: evidence from search asymmetries. , 1988, Psychological review.

[11]  C. Koch,et al.  Some reflections on visual awareness. , 1990, Cold Spring Harbor symposia on quantitative biology.

[12]  Subutai Ahmad,et al.  VISIT: A Neural Model of Covert Visual Attention , 1991, NIPS.

[13]  John K. Tsotsos,et al.  Modeling Visual Attention via Selective Tuning , 1995, Artif. Intell..

[14]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[15]  Thierry Pun,et al.  Attentive mechanisms for dynamic and static scene analysis , 1995 .

[16]  Shumeet Baluja,et al.  Expectation-based selective attention for visual monitoring and control of a robot vehicle , 1997, Robotics Auton. Syst..

[17]  Thomas A. Albert,et al.  Threshold selection using a minimal histogram entropy difference , 1997 .

[18]  B. S. Manjunath,et al.  Peer group filtering and perceptual color image quantization , 1999, ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349).

[19]  Harry Shum,et al.  Statistical Learning of Multi-view Face Detection , 2002, ECCV.

[20]  HongJiang Zhang,et al.  A user attention model for video summarization , 2002, MULTIMEDIA '02.

[21]  HongJiang Zhang,et al.  A model of motion attention for video skimming , 2002, Proceedings. International Conference on Image Processing.

[22]  Bo Zhang,et al.  An effective region-based image retrieval framework , 2002, MULTIMEDIA '02.

[23]  Ying Li,et al.  Salient region detection and tracking in video , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[24]  Xing Xie,et al.  A visual attention model for adapting images on small displays , 2003, Multimedia Systems.