Abstract
In this paper, we propose an image classification system employing multiple modules. The proposed system hierarchically categorizes given sports images into one of the predefined sports classes, eight in this experiment. The image first categorized into one of the two classes in the global module. The corresponding local module is selected accordingly, and then used in the local classification step. By employing multiple modules, the system can specialize each local module properly for the given class feature. The simulation results show that the proposed system successfully classifies images with the correct rate of over 70%.
This paper is supported by Seoul R&BD Program.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Jacobs, R.A., Jordan, M.I., Barto, A.G.: Task decomposition through competition in a modular connectionist architecture: The what and where vision task. Cognitive Science 15, 219–250 (1991)
Waibel, A., Sawai, H., Shikano, K.: Modulraity and scaling in large phonemic neural networks. IEEE Transaction on Acoustics, Speech and Signal Processing 37, 1888–1897 (1989)
Reilly, D.L., Scofield, C., Elbaum, C., Copper, L.N.: Learning system architectures composed of multiple learning modules. In: IEEE Int’l. Conf. Neural Networks, pp. 495–503 (1989)
Jacobs, R.A., Jordan, M.I.: Hierarchical mixtures of experts and the EM algorithm. Neural Computation 6, 181–214 (1994)
Smieja, F.J.: Multiple network systems Minos modules: Task division and module dis-crimination. In: Proc. Of the 8th AISB conference on Artificial Intelligence, Leeds (April 1991)
Pratt, L.Y., Kamm, C.A.: Improving phoneme classification neural network through problem decomposition. In: IEEE Int’l. Joint Conf. Neural Networks, pp. 821–826 (1991)
Osherson, D.N., Weinstein, S., Stoli, M.: Modular learning. Computational Neuro-science, 369–377 (1990)
Haykins, S.: Neural Networks: A comprehensive Foundation. IEEE Press, Macmillan (1994)
Gorkani, M., Picard, R.W.: Textual orientation for sorting photos at a glance. In: Proceedings of International Conference on Pattern recognition, Jerusalem, Israel, October 1994, pp. 459–464 (1994)
Vailaya, A., Jain, A., Zhang, H.J.: On image classification: city images vs. land-scapes. In: Proceedings of IEEE Workshop on Content – Based Access of Image and Video Libraries (December 1998)
Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: Proceedings of IEEE International Workshop on Content – Based Access of Image and Video Libraries, Bombay, India (December 1998)
Ohta, Y.-I., Kanade, T., Sakai, T.: Color information for region segmentation. Computer Graphics and Image Processing 13(3), 222–241 (1980)
Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of im-age data. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(8) (August 1996)
Pass, G., Zabih, R.: Histogram refinement for content-based image retrieval. In: Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision, Sara-sota, Florida, USA (December 1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, W., Oh, S., Kang, S., Kim, D. (2006). Multi-module Image Classification System. In: Larsen, H.L., Pasi, G., Ortiz-Arroyo, D., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2006. Lecture Notes in Computer Science(), vol 4027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11766254_42
Download citation
DOI: https://doi.org/10.1007/11766254_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34638-8
Online ISBN: 978-3-540-34639-5
eBook Packages: Computer ScienceComputer Science (R0)