AUTOENCODER NEURAL NETWORKS: A Performance Study Based on Image Reconstruction, Recognition and Compression - Softcover

Tan, Chun Chet

Softcover

ISBN 10: 3838309464 ISBN 13: 9783838309460

Publisher: LAP Lambert Academic Publishing, 2009

Synopsis

Autoencoders are feedforward neural networks which can have more than one hidden layer. These networks attempt to reconstruct the input data at the output layer. Since the size of the hidden layer in the autoencoders is smaller than the size of the input data, the dimensionality of input data is reduced to a smaller-dimensional code space at the hidden layer. However, training a multilayer autoencoder is tedious. This is due to the fact that the weights at deep hidden layers are hardly optimized. The research work has focused on the characteristics, training and performance evaluation of autoencoders. The concepts of stacking and Restricted Boltzmann Machine have also been discussed in detail. Two datasets, namely ORL face dataset and MNIST handwritten digit dataset have been employed in these experiments. The performances of the autoencoders have also been compared with that of PCA. It has been shown that the autoencoders can also be used for image compression. The compression efficiency has been studied using DDSM dataset (mammogram dataset). Since image patches were used for training, it was possible to compress and decompress mammograms of different sizes.

About this title

About the Author

C. C. Tan: received B.Sc. degree from University of Malaya, and M.IT degree from Multimedia University, Malaysia. He is a lecturer in Faculty of I.T., Multimedia University. Dr. C. Eswaran: received B.Tech, M.Tech and Ph.D degrees from the Indian Institute of Technolgy Madras, India. He is a Professor in Faculty of I.T., Multimedia University.

Edition details

• Publisher: LAP Lambert Academic Publishing
• Publication date: 2009
• Language: English
• ISBN 10: 3838309464
• ISBN 13: 9783838309460
• Binding: Paperback
• Number of pages: 96