Abstract
Human vision system can understand images quickly and accurately, but it is impossible to design a generic computer vision system to challenge this task at present. The most important reason is that computer vision community is lack of effective collaborations with visual psychologists, because current object recognition systems use only a small subset of visual cognition theory. We argue that it is possible to put forward a generic solution for image object recognition if the whole vision cognition theory of different schools and different levels can be systematically integrated into an inherent computing framework from the perspective of computer science. In this paper, we construct a generic object recognition solution, which absorbs the pith of main schools of vision cognition theory. Some examples illustrate the feasibility and validity of this solution.
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References
Li, J., et al.: Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(10), 14 (2003)
Rock, I.: Perception. Scientific American Books, Inc. (1984)
Marr, D.: Vision. Freeman, New York (1982)
Aimin, W., et al.: Method for Qualitatively Evaluating CVIR Algorithms Based on Human Similarity Judgments. In: Proceedings of 7th ICSP, pp. 910–913 (2004)
Itti, L., et al.: A Model of Saliency-based Visual Attention for Rapid Scene Analysis. IEEE Transactions PAMIÂ 20(11) (1998)
Navalpakkam, V., Itti, L.: A Goal Oriented Attention Guidance Model. In: Bülthoff, H.H., Lee, S.-W., Poggio, T.A., Wallraven, C. (eds.) BMCV 2002. LNCS, vol. 2525, pp. 453–461. Springer, Heidelberg (2002)
Oliva, A., Castelhano, M., Henderson, J.: Top-down Control of Visual Attention in Object Detection. In: International Conference on Image Processing (2003)
Walther, D., et al.: Selective Visual Attention Enables Learning and Recognition of Multiple Objects in Cluttered Scenes. Journal of CVIU 2005 (2005) (accepted)
Gregory, R.L.: Eye and Brain. Princeton University Press, Princeton (1997)
Dickinson, S., et al.: Panel Report: The Potential of Geons for Generic 3-D Object Recognition. Image and Vision Computing 15(4), 277–292 (1997)
Brand, M.: Physics Based on Visual Understanding. Computer Vision And Image Understanding 65(2), 192–205 (1997)
Kim, Z.W., et al.: An Automatic Description of Complex Building from Multiple Images. J. of Computer Vision and Image Understanding 96, 60–95 (2004)
Forsyth, D.A., et al.: Computer Vision: A modern Approach. Prentice Hall, Inc, Englewood Cliffs (2003)
Biederman, I.: Recognition-by-Components: A Theory of Human Image Understanding. Psychological Review 94, 115–147 (1987)
Hummel, J.E., et al.: Dynamic Binding in a Neural Network for Shape Recognition. Psychological Review 99, 480–517 (1992)
Nguyen, Q.-L., et al.: Representing 3-D Objects in Range Images Using Geons. J. of Computer Vision and Image Understanding 63(1), 158–168 (1996)
Sarkar, S., et al.: Using Perceptual Inference Networks to Manage Vision Processes. Computer Vision and Image Understanding 62(1), 27–46 (1995)
Treisman, A., Gelade, G.: A Feature Integration Theory of Attention. Cognitive Psychology 12, 97–136 (1980)
Hua-Lin, W., Chowdhury, M.U.: Image Semantic Classification by Using SVM. Journal of Software 14(11), 1891–1899 (2003)
Barnard, K., Duygulub, P., Forsyth, D.: Recognition as Translating Images into Text. In: Internet Imaging IV, Santa Clara, CA, USA (January 2003)
Pentland: Perceptual Organization and The Representation of Natural Form. Artificial Intelligence 28, 293–331 (1986)
Biederman, I.: Visual Object Recognition. In: Visual Cognition, 2nd edn., vol. 2, ch. 4, pp. 121–165. MIT Press, Cambridge (1995)
Xing, W., et al.: Superquadric-based Geons Recognition Utilizing Support Vector Machine. In: Proceedings of 7th ICSP, pp. 1264–1267 (2004)
Koffka, K.: Principle of Gestalt Psychology. Harcourt Brace Jovanovich Company, New York (1935)
Watt, R.J., Phillips, W.A.: The Function of Dynamic Grouping. Trends in Cognition Science 4(12) (December 2000)
Rui, Y., Hunag, T.S.: Relevance Feedback Technique in Image Retrivel. In: Lew, M.S. (ed.) Principles of Visual Information Retrieval, ch. 9, pp. 219–258. Springer, Heidelberg (2001)
Ming, Z.: Psychology of Visual Cognition. East China Normal University Press (1999)
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Wu, A., Xu, D., Yang, X., Zheng, J. (2005). Generic Solution for Image Object Recognition Based on Vision Cognition Theory. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_166
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DOI: https://doi.org/10.1007/11540007_166
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
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