Learning Human-Like Color Categorization through Interaction

Authors: Rinaldo Christian Tanumara, Ming Xie, Chi Kit Au

Abstract:

Human perceives color in categories, which may be identified using color name such as red, blue, etc. The categorization is unique for each human being. However despite the individual differences, the categorization is shared among members in society. This allows communication among them, especially when using color name. Sociable robot, to live coexist with human and become part of human society, must also have the shared color categorization, which can be achieved through learning. Many works have been done to enable computer, as brain of robot, to learn color categorization. Most of them rely on modeling of human color perception and mathematical complexities. Differently, in this work, the computer learns color categorization through interaction with humans. This work aims at developing the innate ability of the computer to learn the human-like color categorization. It focuses on the representation of color categorization and how it is built and developed without much mathematical complexity.

Keywords: Color categorization, color learning, machinelearning, color naming.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1077774

References:

[1] R. W. Hunt, Measuring Colour. Fountain Press, 1998.
[2] L. Steels and T. Belpame, "Coordinating perceptually grounded categories through language: A case study for colour," Behavioral and Brain Sciences, vol. 28, pp. 469-529, 2005.
[3] C. Breazeal, "Toward Sociable Robot," Robotics and Autonomous Systems, vol. 42, pp. 167-175, 2003.
[4] B. Berlin and P. Kay, Basic color terms: Their universality and evolution. University of California Press, 1969.
[5] P. Kay and T. Regier, "Resolving the question of color naming universals," Proceedings of the National Academy of Science, vol. 100, no. 15, pp. 9085-9089, 2003.
[6] S. Pinker, The language instinct: How the mind creates language. Harper Perennial Modern Classics, 2000.
[7] E. Rosch-Heider, "Universals in Color Naming and Memory," Journal of Experimental Psychology, vol. 93, pp. 10-20, 1972.
[8] S. N. Yendrikhovskij, "Computing color categories for statistics of natural image," Journal of Imaging Science and Technology, vol. 45, no. 5, pp. 409-417, 2001.
[9] A. VanWijk, "A cross-cultural theory of colour and brightness nomenclature," Bijdragen tot de taal-, land- en volkenkunde, vol. 115, pp. 113-137, 1959.
[10] T. Belpaeme, "Simulating the formulation of color categories," presented at International Joint Conference on Artificial Intelligence, Seattle, 2001.
[11] I. R. Davies and G. Corbett, "A cross-cultural study of color grouping: Evidence for weak linguistic relativity," British Journal of Psychology, vol. 88, pp. 493-517, 1997.
[12] K. A. Jameson and N. Alvarado, "The relational correspondence between category exemplars and names," Philosophical Psychology, vol. 10, no. 1, pp. 25-49, 2003.
[13] M. Dowman, "Colour terms, syntax and bayes modelling acquisition and evolution," PhD Thesis, University of Sydney, 2004.
[14] J. M. G. Lammens, "A computational model of color perception and color naming," PhD Thesis, State University of New York, 1994.
[15] M. Xie, J. S. Kandhasamy, and H. F. Chia, "Meaning-centric Framework for Natural Text/Scene Understanding by Robots," International Journal of Humanoid Robotics, vol. 1, no. 2, pp. 375-407, 2004.
[16] S. Harnad, S. J. Hanson, and J. Lubin, "Learned categorical perception in neural nets: Implication for symbol grounding," in Symbolic Processors and Connectionist Network Models in Artificial Intelligence and Cognitive Modeling: Steps Toward Principled Integration, V. Honavar and L. Uhr, Eds. Boston: Academic Press, 1995, pp. 191-206.
[17] S. Usui, S. Nakauchi, and M. Nakano, "Reconstruction of Munsell Color Space by a Five Layered Neural Network," presented at IEEE International Conference on Neural Network, 1990.
[18] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representation by back-propagating errors," Nature, vol. 323, pp. 533- 536, 1986.
[19] X. Yin, D. Guo, and M. Xie, "Hand image segmentation using color and RCE neural network," Robotics and Autonomous Systems, vol. 34, pp. 235-250, 2001.
[20] N. C. Yeo, K. H. Lee, Y. V. Venkatesh, and S. H. Ong, "Colour image segmentation using the self-organizing map and adaptive resonance theory," Image and Vision Computing, vol. 23, pp. 1060-1079, 2005.
[21] A. H. Dekker, "Kohonen neural network for optimal color quantization," Network: Computation in Neural System, vol. 5, pp. 351-367, 1994.
[22] T. Uchiyama and M. A. Arbib, "Color Image Segmentation Using Competitive Learning," IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 16, no. 12, pp. 1197-1206, 1994.
[23] A. Verikas, K. Malmqvist, M. Bachauskene, L. Bergman, and K. Nilsson, "Hierarchical neural network for color classification," presented at IEEE International Conference on Neural Network, 1994.
[24] S. Dehaene, "Evolution of human cortical circuits for reading and arithmetic: The "neuronal recycling" hypothesis.," in From Monkey Brain to Human Brain, S. Dehaene, J. R. Duhamel, M. Hauser, and G. Rizzolati, Eds. Massachusettes: MIT Press, 2004.
[25] M. D. Hauser, N. Chomsky, and W. T. Fitch, "The faculty of language: what is it, who has it, and how did it evolve?," in Science, vol. 298, 2002, pp. 1569-1579.
[26] M. Fairchild, Color appearance model. Addison-Wesley, 1998.
[27] P. Kay and L. Maffi, "Color appearance and the emergence and evolution of basic color lexicons," American Anthropologist, vol. 101, no. 4, pp. 743-760, 1999.
[28] J. Davidoff, "Language and perceptual categorization," Trends in Cognitive Sciences, vol. 5, no. 9, pp. 382-387, 2001.
[29] J. Davidoff, I. Davies, and D. Roberson, "Color categories in a stone-age tribe," Nature, vol. 398, pp. 203-204, 1999.
[30] S. Grossberg and D. Todorovic, "Neural dynamics of 1-D and 2-D brightness perception: A unified model of classical and recent phenomena," Perception and Psychophysics, vol. 43, pp. 241-277, 1988.