Associative neural networks: properties, learning, and applications.

by Chi-sing Leung.Thesis (Ph.D.)--Chinese University of Hong Kong, 1994.Includes bibliographical references (leaves 236-244).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Background of Associative Neural Networks --- p.1Chapter 1.2 --- A Distributed Encoding Model: Bidirectional Associative Memory --- p.3Chapter 1.3 --- A Direct Encoding Model: Kohonen Map --- p.6Chapter 1.4 --- Scope and Organization --- p.9Chapter 1.5 --- Summary of Publications --- p.13Chapter I --- Bidirectional Associative Memory: Statistical Proper- ties and Learning --- p.17Chapter 2 --- Introduction to Bidirectional Associative Memory --- p.18Chapter 2.1 --- Bidirectional Associative Memory and its Encoding Method --- p.18Chapter 2.2 --- Recall Process of BAM --- p.20Chapter 2.3 --- Stability of BAM --- p.22Chapter 2.4 --- Memory Capacity of BAM --- p.24Chapter 2.5 --- Error Correction Capability of BAM --- p.28Chapter 2.6 --- Chapter Summary --- p.29Chapter 3 --- Memory Capacity and Statistical Dynamics of First Order BAM --- p.31Chapter 3.1 --- Introduction --- p.31Chapter 3.2 --- Existence of Energy Barrier --- p.34Chapter 3.3 --- Memory Capacity from Energy Barrier --- p.44Chapter 3.4 --- Confidence Dynamics --- p.49Chapter 3.5 --- Numerical Results from the Dynamics --- p.63Chapter 3.6 --- Chapter Summary --- p.68Chapter 4 --- Stability and Statistical Dynamics of Second order BAM --- p.70Chapter 4.1 --- Introduction --- p.70Chapter 4.2 --- Second order BAM and its Stability --- p.71Chapter 4.3 --- Confidence Dynamics of Second Order BAM --- p.75Chapter 4.4 --- Numerical Results --- p.82Chapter 4.5 --- Extension to higher order BAM --- p.90Chapter 4.6 --- Verification of the conditions of Newman's Lemma --- p.94Chapter 4.7 --- Chapter Summary --- p.95Chapter 5 --- Enhancement of BAM --- p.97Chapter 5.1 --- Background --- p.97Chapter 5.2 --- Review on Modifications of BAM --- p.101Chapter 5.2.1 --- Change of the encoding method --- p.101Chapter 5.2.2 --- Change of the topology --- p.105Chapter 5.3 --- Householder Encoding Algorithm --- p.107Chapter 5.3.1 --- Construction from Householder Transforms --- p.107Chapter 5.3.2 --- Construction from iterative method --- p.109Chapter 5.3.3 --- Remarks on HCA --- p.111Chapter 5.4 --- Enhanced Householder Encoding Algorithm --- p.112Chapter 5.4.1 --- Construction of EHCA --- p.112Chapter 5.4.2 --- Remarks on EHCA --- p.114Chapter 5.5 --- Bidirectional Learning --- p.115Chapter 5.5.1 --- Construction of BL --- p.115Chapter 5.5.2 --- The Convergence of BL and the memory capacity of BL --- p.116Chapter 5.5.3 --- Remarks on BL --- p.120Chapter 5.6 --- Adaptive Ho-Kashyap Bidirectional Learning --- p.121Chapter 5.6.1 --- Construction of AHKBL --- p.121Chapter 5.6.2 --- Convergent Conditions for AHKBL --- p.124Chapter 5.6.3 --- Remarks on AHKBL --- p.125Chapter 5.7 --- Computer Simulations --- p.126Chapter 5.7.1 --- Memory Capacity --- p.126Chapter 5.7.2 --- Error Correction Capability --- p.130Chapter 5.7.3 --- Learning Speed --- p.157Chapter 5.8 --- Chapter Summary --- p.158Chapter 6 --- BAM under Forgetting Learning --- p.160Chapter 6.1 --- Introduction --- p.160Chapter 6.2 --- Properties of Forgetting Learning --- p.162Chapter 6.3 --- Computer Simulations --- p.168Chapter 6.4 --- Chapter Summary --- p.168Chapter II --- Kohonen Map: Applications in Data compression and Communications --- p.170Chapter 7 --- Introduction to Vector Quantization and Kohonen Map --- p.171Chapter 7.1 --- Background on Vector quantization --- p.171Chapter 7.2 --- Introduction to LBG algorithm --- p.173Chapter 7.3 --- Introduction to Kohonen Map --- p.174Chapter 7.4 --- Chapter Summary --- p.179Chapter 8 --- Applications of Kohonen Map in Data Compression and Communi- cations --- p.181Chapter 8.1 --- Use Kohonen Map to design Trellis Coded Vector Quantizer --- p.182Chapter 8.1.1 --- Trellis Coded Vector Quantizer --- p.182Chapter 8.1.2 --- Trellis Coded Kohonen Map --- p.188Chapter 8.1.3 --- Computer Simulations --- p.191Chapter 8.2 --- Kohonen MapiCombined Vector Quantization and Modulation --- p.195Chapter 8.2.1 --- Impulsive Noise in the received data --- p.195Chapter 8.2.2 --- Combined Kohonen Map and Modulation --- p.198Chapter 8.2.3 --- Computer Simulations --- p.200Chapter 8.3 --- Error Control Scheme for the Transmission of Vector Quantized Data --- p.213Chapter 8.3.1 --- Motivation and Background --- p.214Chapter 8.3.2 --- Trellis Coded Modulation --- p.216Chapter 8.3.3 --- "Combined Vector Quantization, Error Control, and Modulation" --- p.220Chapter 8.3.4 --- Computer Simulations --- p.223Chapter 8.4 --- Chapter Summary --- p.226Chapter 9 --- Conclusion --- p.232Bibliography --- p.23

Multi-image classification and compression using vector quantization

Vector Quantization (VQ) is an image processing technique based on statistical clustering, and designed originally for image compression. In this dissertation, several methods for multi-image classification and compression based on a VQ design are presented. It is demonstrated that VQ can perform joint multi-image classification and compression by associating a class identifier with each multi-spectral signature codevector. We extend the Weighted Bayes Risk VQ (WBRVQ) method, previously used for single-component images, that explicitly incorporates a Bayes risk component into the distortion measure used in the VQ quantizer design and thereby permits a flexible trade-off between classification and compression priorities. In the specific case of multi-spectral images, we investigate the application of the Multi-scale Retinex algorithm as a preprocessing stage, before classification and compression, that performs dynamic range compression, reduces the dependence on lighting conditions, and generally enhances apparent spatial resolution. The goals of this research are four-fold: (1) to study the interrelationship between statistical clustering, classification and compression in a multi-image VQ context; (2) to study mixed-pixel classification and combined classification and compression for simulated and actual, multispectral and hyperspectral multi-images; (3) to study the effects of multi-image enhancement on class spectral signatures; and (4) to study the preservation of scientific data integrity as a function of compression. In this research, a key issue is not just the subjective quality of the resulting images after classification and compression but also the effect of multi-image dimensionality on the complexity of the optimal coder design

Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further