A Study on the Image Classification Techniques Based on Wavelet Artificial Neural Network Algorithm

Abstract:

With the ever-changing education information technology, it is a big problem for the universities and college that how to classify the thousands of copies of the image during the art examination marking process. This paper is to explore the application of artificial intelligence techniques, and to do accurate classification of a large number of images within a limited time and under the help of computer. It is can be seen that the proposed method is feasible through the application of the results of the actual work. Artificial neural network training Artificial neural network training methods have two mainly style, which are Incremental Training and Batch Training, and take the amount of different network training mission as the distinction standard. First, to introduce the Incremental Training [1], that means whenever the network receives the input vector and target vector, it have to adjust once the connection weights and thresholds. It is an online learning method. The other one is Batch Training [2], that means no longer adjust the connection and immediately, but perform bulk adjustment, and after a given volume of the input vector and target vector. Both training methods can be applied, whether it is static or dynamic neural network. Different results will be obtained by artificial neural network for the use of different training methods. When using artificial neural networks to solve specific problems, learning methods, training methods and artificial neural network function should be selected according to the expected results of question type and its specific requirements [3-4]. The selection of parameters of wavelet neural networks and adaptive learning

• Periodical: Applied Mechanics and Materials (Volumes 602-605)
• Pages: 3512-3514
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.602-605.3512
• Online since: August 2014
• Authors: Xue Ding*, Hong Hong Yang
• Keywords: Image Classification Techniques, Wavelet Artificial Neural Network Algorithm

References

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DOI: 10.1109/icnc.2007.671

[3] Yuan Zengren. 2003. Artifical Intelligence Principles and Applications. Beijing: Tsinghua University Press.

[4] Xiang Xiaodong. 2005. Wavelet Neural Network Prediction Methods and Application. Chinese Journal of Scientific Instrument. No. 6: 14-15.