Minimizing binding errors using learned conjunctive features

Neural Computation

Volumn 12, Issue 4, 2000, Pages 731-762

  Mel, Bartlett W ^a   Fiser, József ^b  

^a University of Southern California ^*  (United States)

^b UNIVERSITY OF ROCHESTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; LANGUAGE; PATTERN RECOGNITION;

ARTIFICIAL INTELLIGENCE; LANGUAGE; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, VISUAL;

EID: 0034170073     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976600300015574     Document Type: Article

References (43)

1. Barron, R., Mucciardi, A., Cook, F., Craig, J., & Barron, A. (1984). Adaptive learning networks: Development and Applications in the United States of algorithms related to GMDH. In S. Farrow (Ed.), Self-organizing methods in modeling. New York: Marcel Dekker.
2. Biederman, I. (1995). Visual object recognition. In S. Kosslyn & D. Osherson (Eds.), An invitation to cognitive science (2nd ed.) (pp. 121-165). Cambridge, MA: MIT Press.
3. Biederman, I., & Gerhardstein, P. (1995). Viewpoint-dependent mechanisms in visual object recognition: Reply to Tarr and Bülthoff (1995). J. Exp. Psychol. (Human Perception and Performance), 21, 1506-1514.
4. Califano, A., & Mohan, R. (1994). Multidimensional indexing for recognizing visual shapes. IEEE Trans. on PAMI, 16, 373-392.
5. Charniak, E. (1993). Statistical language learning. Cambridge, MA: MIT Press.
6. Douglas, R., & Martin, K. (1998). Neocortex. In G. Shepherd (Ed.), The synaptic organization of the brain (pp. 567-509). Oxford: Oxford University Press.
7. Edelman, S., & Duvdevani-Bar, S. (1997). A model of visual recognition and categorization. Phil. Trans. R Soc. Lond. [Biol], 352, 1191-1202.
8. Fahlman, S., & Lebiere, C. (1990). The cascade-correlation learning architecture. In D. Touretzky (Ed.), Advances in neural information processing systems, 2 (pp. 524-532). San Mateo, CA: Morgan Kaufmann.
9. Fize, D., Boulanouar, K., Ranjeva, J., Fabre-Thorpe, M., & Thorpe, S. (1998). Brain activity during rapid scene categorization - a study using event-related FMRI. J. Cog. Neurosci., suppl S, 72-72.
10. Fukushima, K., Miyake, S., & Ito, T. (1983). Neocognition: A neural network model for a mechanism of visual pattern recognition. IEEE Trans. Sys. Man & Cybernetics, SMC-13, 826-834.
11. Gilbert, C. (1983). Microcircuitry of the visual cortex. Ann. Rev. Neurosci, 89, 8366-8370.
12. Heller, J., Hertz, J., Kjær, T., & Richmond, B. (1995). Information flow and temporal coding in primate pattern vision. J. Comput. Neurosci., 2, 175-193.
13. Hubel, D., & Wiesel, T. (1968). Receptive fields and functional architecture of monkey striate cortex. J. Physiol., 195, 215-243.
14. Hummel, J., & Biederman, I. (1992). Dynamic binding in a neural network for shape recognition. Psych. Rev., 99, 480-517.
15. Johnston, A., Hill, H., & Carman, N. (1992). Recognizing faces: Effects of lighting direction, inversion, and brightness reversal. Perception, 21, 365-375.
16. Jones, E. (1981). Anatomy of cerebral cortex: Columnar input-output relations. In F. Schmitt, F. Worden, G. Adelman, & S. Dennis (Eds.), The organization of cerebral cortex. Cambridge, MA: MIT Press.
17. Kučera, H., & Francis, W. (1967). Computational analysis of present-day American English. Providence, RI: Brown University Press.
18. Lades, M., Vorbruggen, J., Buhmann, J., Lange, J., Malsburg, C., Wurtz, R., & Komen, W. (1993). Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Computers, 42, 300-311.
19. Lang, G. K., & Seitz, P. (1997). Robust classification of arbitrary object classes based on hierarchical spatial feature-matching. Machine Vision and Applications, 10, 123-135.
20. Le Cun, Y., Matan, O., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., Jackel, L., & Baird, H. (1990). Handwritten zip code recognition with multilayer networks. In Proc. of the 10th Int. Conf. on Patt. Rec. Los Alamitos, CA: IEEE Computer Science Press.
21. Logothetis, N., & Pauls, J. (1995). Psychophysical and physiological evidence for viewer-centered object representations in the primate. Cerebral Cortex, 3, 270-288.
22. Logothetis, N., & Sheinberg, D. (1996). Visual object recognition. Ann. Rev. Neurosci., 19, 577-621.
23. Malsburg, C. (1994). The correlation theory of brain function (reprint from 1981). In E. Domany, J. van Hemmen, & K. Schulten (Eds.), Models of neural networks II (pp. 95-119). Berlin: Springer.
24. McClelland, J., & Rumelhart, D. (1981). An interactive activation model of context effects in letter perception. Psych. Rev., 88, 375-407.
25. Mel, B. W. (1997). SEEMORE: Combining color, shape, and texture histogramming in a neurally-inspired approach to visual object recognition. Neural Computation, 9, 777-804.
26. Mel, B. W., Ruderman, D. L., & Archie, K. A. (1998). Translation-invariant orientation tuning in visual "complex" cells could derive from intradendritic computations. J. Neurosci., 17, 4325-4334.
27. Mozer, M. (1991). The perception of multiple objects. Cambridge, MA: MIT Press.
28. Oram, M., & Perrett, D. (1992). Time course of neural responses discriminating different views of the face and head. J. Neurophysiol., 68(1), 70-84.
29. Oram, M. W., & Perrett, D. I. (1994). Modeling visual recognition from neurobiological constraints. Neural Networks, 7(6/7), 945-972.
30. Pitts, W., & McCullough, W. (1947). How we know universals: The perception of auditory and visual forms. Bull. Math. Biophys., 9, 127-147.
31. Potter, M. (1976). Short-term conceptual memory for pictures. J. Exp. Psychol.: Human Learning and Memory, 2, 509-522.
32. Sandon, P., & Urh, L. (1988). An adaptive model for viewpoint-invariant object recognition. In Proc. of the 10th Ann. Conf. of the Cog. Sci. Soc. (pp. 209-215). Hillsdale, NJ: Erlbaum.
33. Schiele, B., & Crowley, J. (1996). Probabilistic object recognition using multidimensional receptive field histograms. In Proc. of the 13th Int. Conf. on Patt. Rec. (Vol. 2 pp. 50-54). Los Alamitos, CA: IEEE Computer Society Press.
34. Swain, M., & Ballard, D. (1991). Color indexing. Int. J. Computer Vision, 7, 11-32.
35. Szentagothai, J. (1977). The neuron network of the cerebral cortex: A functional interpretation. Proc. R. Soc. Lond. B, 201, 219-248.
36. Tanaka, K. (1996). Inferotemporal cortex and object vision. Ann. Rev. Neurosci, 19, 109-139.
37. Thorpe, S., Fize, D., & Marlot, C. (1996). Speed of processing in the human visual system. Nature, 381, 520-522.
38. Van Essen, D. (1985). Functional organization of primate visual cortex. In A. Peters & E. Jones (Eds.), Cerebral cortex (pp. 259-329). New York: Plenum Publishing.
39. Wallis, G., & Rolls, E. T. (1997). Invariant face and object recognition in the visual system. Prog. Neurobiol., 51, 167-194.
40. Weng, J., Ahuja, N., & Huang, T. S. (1997). Learning recognition and segmentation using the cresceptron. Int. J. Comp. Vis., 25(2), 109-143.
41. Wickelgren, W. (1969). Context-sensitive coding, associative memory, and serial order in (speech) behavior. Psych. Rev., 76, 1-15.
42. Yin, R. (1969). Looking at upside down faces. J. Exp. Psychol., 81, 141-145.
43. Zemel, R., Mozer, M., & Hinton, G. (1990). TRAFFIC: Recognizing objects using hierarchical reference frame transformations. In D. Touretzky (Ed.), Advances in neural information processing systems, 2 (pp. 266-273). San Mateo, CA: Morgan Kaufmann.