Cascaded sequential attention for object recognition with informative local descriptors and q-learning of grouping strategies

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Volumn 2005-September, Issue , 2005, Pages

  Paletta, Lucas ^a   Fritz, Gerald ^a   Seifert, Christin ^a  

^a JOANNEUM RESEARCH   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; INFORMATION THEORY; MARKOV PROCESSES; OBJECT DETECTION; REINFORCEMENT LEARNING;

EXTRACTION OF INFORMATION; GEOMETRIC RELATIONS; GLOBAL INFORMATIONS; GROUPING STRATEGIES; INFORMATION MAXIMIZATION; LOCAL IMAGE DESCRIPTORS; MARKOV DECISION PROCESSES; REAL WORLD ENVIRONMENTS;

OBJECT RECOGNITION;

EID: 85114727327     PISSN: 21607508     EISSN: 21607516     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2005.429     Document Type: Conference Paper

References (20)

1. J.D. Schall and K.G. Thompson, “Neural selection and control of visually guided eye movements,” Annual Review of Neuroscience 22:,, no. 22, pp. 241-259, 1999.
2. G. Deco, “The computational neuroscience of visual cognition: Attention, memory and reward,” in Proc. International Workshop on Attention and Performance in Computational Vision, 2004, pp. 49-58.
3. A. Gorea and D. Sagi, “Selective attention as the substrate of optimal decision behaviour in environments with multiple stimuli,” in Proc. European Conference on Visual Perception, 2003.
4. J.M. Henderson, “Human gaze control in real-world scene perception,” Trends in Cognitive Sciences, vol. 7, pp. 498 -504, 2003.
5. H. Deubel, “Localization of targets across saccades: Role of landmark objects,” Visual Cognition,, no. 11, pp. 173-202, 2004.
6. L. W. Stark and Y. S. Choi, “Experimental metaphysics: The scanpath as an epistemological mechanism,” in Visual attention and cognition, W. H. Zangemeister, H. S. Stiehl, and C. Freska, Eds., pp. 3-69. Elsevier Science, Amsterdam, Netherlands, 1996.
7. I.A. Rybak, I. Gusakova V., A.V. Golovan, L.N. Podladchikova, and N.A. Shevtsova, “A model of attention-guided visual perception and recognition,” Vision Research, vol. 38, pp. 2387-2400, 1998.
8. R.A. Rensink, J.K. O'Regan, and J.J. Clark, “To see or not to see: The need for attention to perceive changes in scenes,” Psychological Science, vol. 8, pp. 368-373, 1997.
9. M. Li and J.J. Clark, “Learning of position and attention-shift invariant recognition across attention shifts,” in Proc. International Workshop on Attention and Performance in Computational Vision, 2004, pp. 41-48.
10. C. Bandera, F.J. Vico, J.M. Bravo, M.E. Harmon, and L.C. Baird III, “Residual Q-learning applied to visual attention,” in International Conference on Machine Learning, 1996, pp. 20-27.
11. S. Minut and S. Mahadevan, “A reinforcement learning model of selective visual attention,” in Proc. International Conference on Autonomous Agents, 2001, pp. 457-464.
12. G. Fritz, C. Seifert, and L. Paletta, “Learning informative SIFT descriptors for object detection,” in Proc. International Conference on Image Processing, submitted to ICIP 2005.
13. M. Weber, M. Welling, and P. Perona, “Unsupervised learning of models for recognition,” in Proc. European Conference on Computer Vision, 2000, pp. 18-32.
14. David G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
15. G. Fritz, L. Paletta, and H. Bischof, “Object recognition using local information content,” in Proc. International Conference on Pattern Recognition, ICPR 2004. Cambridge, UK, 2004, vol. II, pp. 15-18.
16. L. Itti and C. Koch, “Computational modeling of visual attention,” Nature Reviews Neuroscience, vol. 2, no. 3, pp. 194-203, Mar 2001.
17. G. Fritz, C. Seifert, L. Paletta, and H. Bischof, “Rapid object recognition from discriminative regions of interest,” in Proc. National Conference on Artificial Intelligence, AAAI 2004. San Jose, CA, 2004, pp. 444-449.
18. S.P. Tipper, S. Grisson, and K. Kessler, “Long-term inhibition of return of attention,” Psychological Science, vol. 14, pp. 19-25-105, 2003.
19. M.L. Puterman, Markov Decision Processes, John Wiley & Sons, New York, NY, 1994.
20. C. Watkins and P. Dayan, “Q-learning,” Machine Learning, vol. 8, no. 3,4, pp. 279-292, 1992.