Improved Neural Network Training Algorithm for Classification of Compressed and Uncompressed Images
نویسنده
چکیده
For managing data in a smart card’s limited memory, containing medical and biometric images, images compression is resorted to. For image retrieval, it is necessary that the classification algorithm be efficient to search and locate the image in a compressed domain. This study proposes a novel training algorithm for Multi-Layer Perceptron Neural Network (MLP-NN) to classify compressed images. MLP-NN is used for image classification. The most common training algorithm is Error Back Propagation algorithm (EBP) with a very poor convergence rate. Other second order approaches including Levenberg-Marquardt method and conjugate gradient method are better to train neural networks. This study proposes an improved training algorithm based on Levenberg-Marquardt method for Neural Network.
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