Image Classification by Combining Wavelet Transform and Neural Network

In this article, the authors have proposed a method of classification of image by combining wavelet transform and neural network. Their main objective in this work is to achieve an optimal approach of classification by combining wavelet transform and neural network. The proposed scheme for successful classification is combination of a wavelet domain feature extractor and back propagation neural networks classifier. For achieving a suitable way for classification of image here, the authors first use wavelet transform which will decompose their main image into sub image (10) and after that, this decomposed image are in turn analyzed and finally features are extracted. In this proposed method of image classification, first, they divide all given image into six parts. For obtaining the necessary and required information from each part of the given divided image, they use first order movements of colour (9) and daubechies 4 types of wavelet transform. Resulting data consist of 98% and 90% efficiency for training and testing respectively.