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Image Recognition With the Help of Auto-Associative Neural Network
Moumi Pandit, Mousumi Gupta
Pages - 54 - 63     |    Revised - 31-03-2011     |    Published - 04-04-2011
Volume - 5   Issue - 1    |    Publication Date - March / April 2011  Table of Contents
Image Recognition, Associative Memory, Pattern Matching, Artificial Neural Network, Weight Matrix
This paper proposes a Neural Network model that has been utilized for image recognition. The main issue of Neural Network model here is to train the system for image recognition. In this paper the NN model has been prepared in MATLAB platform. The NN model uses Auto-Associative memory for training. The model reads the image in the form of a matrix, evaluates the weight matrix associated with the image. After training process is done, whenever the image is provided to the system the model recognizes it appropriately. The weight matrix evaluated here is used for image pattern matching. It is noticed that the model developed is accurate enough to recognize the image even if the image is distorted or some portion/ data is missing from the image. This model eliminates the long time consuming process of image recognition
CITED BY (5)  
1 Jain, M., & Tripathi, K. C. Auto Associative Neural Networks for Nonlinear Principal Components Analysis of Sea Surface Temperature Anomalies in Indian Ocean.
2 Demarinis, F., Accettura, A., Garzia, F., & Cusani, R. (2014, October). Automatic security system for recognizing unexpected motions through video surveillance. In Security Technology (ICCST), 2014 International Carnahan Conference on (pp. 1-5). IEEE.
3 Zambrano, J. G., Guzmán-Ramírez, E., & Pogrebnyak, O. (2013). Search Algorithm for Image Recognition Based on Learning Algorithm for Multivariate Data Analysis. INTECH Open Access Publisher.
4 Philosophiae, d. (2012). mlungisi sizwe duma (doctoral dissertation, university of johannesburg).
5 Lernatovych, DA, & Kondratenko, Y. (2012). Identification by image element comparisons. Artificial Intelligence.
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2 Academic Journals Database
3 CiteSeerX
4 refSeek
6 Libsearch
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1 FOR JOURNALS: M. Burl, T. Leung, and P. Persona. "Recognition of Planar Object Classes". In Proc. IEEE Comput. Soc. Con! Computer. Vision and Pattern Recognition., 223-230 ,1996.
2 FOR JOURNALS : H. Waukee, H. Harada, et al. “An Architecture of Self-Organizing Map for Temporal Processing and Its Application to a Braille Recognition Task,” IEICE Trans. on Information and Systems, J87(3), 884–892, 2004.
3 FOR JOURNALS : Wang, D. L., & Terman, D. "Locally excitatory globally inhibitory oscillator networks." IEEE Trans. Neural Networks, 6, 283-286 1995.
4 FOR JOURNALS : José R. Dorronsoro, Vicente López, Carlos Santa Cruz, and Juan A. Sigüenza, “Autoassociative Neural Networks and Noise Filtering” IEEE transactions on signal processing, 51( 5) , 2003
5 FOR JOURNALS: S. Palanivel, B.S. Venkatesh and B. Yegnanarayana, Real time face recognition system using autoassociative Neural Network models, ICASSP 2003.
6 FOR JOURNALS: S. Amari “Neural Theory of association and concept formation” Biological cybernetics, 26, 175-185,1977.
7 FOR CONFERENCES : Csurka, G., Dance, c., Bray, c., and Fan, L., "Visual categorization with bags of key points," In Proceedings Workshop on Statistical Learning in Computer Vision, I -22, 2004.
8 FOR BOOKS: Simon Haykin, “Neural Networks A Comprehensive Foundation”, Pearson Educartion (Singapore) Pvt. Ltd. pp. 1-49 (2004).
9 FOR BOOKS : S.N. Sivanandam, S. Sumathi, S.N. Deepa, “Introduction to Neural Networks using Matlab 6.0”, Tata McGraw-Hill pp. 10-29, pp 109-165(2006).
Miss Moumi Pandit
Sikkim Manipal Institute of Technology - India
Dr. Mousumi Gupta
- India