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THESIS (6.743Mb) University of Plymouth PEARL https://pearl.plymouth.ac.uk 04 University of Plymouth Research Theses 01 Research Theses Main Collection 1992 CLASSIFICATION OF COMPLEX TWO-DIMENSIONAL IMAGES IN A PARALLEL DISTRIBUTED PROCESSING ARCHITECTURE SIMPSON, ROBERT GILMOUR http://hdl.handle.net/10026.1/1843 University of Plymouth All content in PEARL is protected by copyright law. Author manuscripts are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author. CLASSIFICATION OF COMPLEX TWO-DIMENSIONAL IMAGES IN A PARALLEL DISTRIBUTED PROCESSING ARCHITECTURE ROBERT GILMOUR SIMPSON, B.A., M.Sc A thesis submitted in partial fulfilment of the requirements of the University of Plymouth for the degree of Doctor of Philosophy October 1992 University of Plymouthin collaboration with the Plymouth Marine Laboratory REFERENCE ONLY 90 0166561 3 LIBRARY STORE UNIVERSITY OF PLYMOUTH ir UBRARY SERVICES 006 .3^ S-XM Contl To all my family, for their encouragement, ROBERT GILMOUR SIMPSON Abstract. Neural network analysis is proposed and evaluated as a method of analysis of marine biological data, specifically images of plankton specimens. The quantification of the various plankton species is of great scientific importance, from modelling global climatic change to predicting the economic effects of toxic red tides. A preliminary evaluation of the neural network technique is made by the development of a back-propagation system that successfully learns to distinguish between two co-occurring morphologically similar species from the North Atlantic Ocean, namely Ceratium arcticum and C. longipes. Various techniques are developed to handle the indeterminately labelled source data, pre-process the images and successfully train the networks. An analysis of the network solutions is made, and some consideration given to how the system might be extended. Acknowledgements pag^ 1 Author's declaration P^8^ ^ Chapter 1. Neural Networks page 3 1.1 Introduction page 3 1.2 Image Processing page 3 1.3 Parallel Distributed Processing page 7 1.3.1 Parallel processing page 7 1.3.2 Parallel distributed processing page 8 1.3.2.1 Nodes page 10 13.2.2 Connections poge 12 1.3.2.3 Network functions P<'8^ i 2 1.4 Neural Network Implementations page 15 1.4.1 The artificial neuron page 15 1.4.2 The perceptron page 16 1.4.2.1 Rosenblatt's formulation page 16 1.4.2.2 Minsky and Paperi's criticism of the perceptron page 20 1.4.3 Multiple constraint satisfaction page 23 1.4.4 Multi-layer networks page 25 1.4.4.2 Back-propagation page 26 1.5 Summary page 30 Chapter 2. Classification page 31 2.1 Introduction page 31 2.2 Classification page 31 2.3 Theories of Categorisation page 33 2.3.1 The classical model page 34 2.3.1.1 Defining features page 34 2.3.1.2 Implementing classical theory page 35 2.3.1.3 Failures of the classical model page 37 2.3.2 Qasses of categories page 39 2.3.3 Probabilistic models of categorisation page 40 2.3.3.1 Prototype models page 40 2.3.3.2 Exemplar models page 41 2.3.3.3 Feature list models page 42 2.3.3.4 Type and token representation in neural networks page 44 2.3.4 S'miilarity of individuals page 46 2.3.4.1 Similarity theory page 46 2.3.4.2 The Ugly Duckling theorem page 48 2.3.5 Psychological measurement page 51 2.3.6 Formal models of categorisadon page 53 2.3.6.1 Bayesian theory page 53 2.3.6.2 Fisher's discriminant page 56 2.3.6.3 Least Mean Sqitared error method page 57 2.3.6.4 Nearest Neighbour methods page 58 2.3.7 Modelling a classification scheme page 62 2.4 Summary page 64 Chapter 3. Rationales page 65 3.1 Introduction page 65 3.2 Previous Work in the Domain page65^ 3.2:1 Tradidonal analytical techniques page 66 3.2.2 Automation of moiphometric analysis page 68 3.2.2.1 Segmentation of the image page 68 3.2.2.2 Classifying the single specimen page 69 3.2.3 Other automated techniques page 70 3.2.4 Summary of previous work in the domain page 71 3.3 Network algorithm selection page 72 3.3.1 Unsupervised networks page 73 3.3J.1 The competitive network poge 73 3.3.1.2 Experiments with an ART I model page 75 3.3.2 Supervised networks page 78 3.3.2.1 The back-propagation algorithm page 79 3.3.2.2 Feed-forward networks poge 80 3.4 Selection of pre-processing page 80 3.4.1 The need for pre-processing page 80 3.4.2 Pre-processing techniques page 81 3.4.3 Spatial frequency trials page 82 3.43.1 Results page 84 3.4.4 Using Fourier information page 85 3.5 Summary page 86 Chapter 4. The Plankton Data Set page 87 4.1 Introduction page 87 4.2 The Problem Domain - Plankton page 87 4.2.1 Ceratium plankton page 87 4.3 Assessing the Data page 89 4.3.1 Ground truth in classifying data page 89 4.3.2 Obtaining expert judgement page 91 4.3.3 Assessing expert judgement page 92 4.4 Pre-processing the Data page 96 4.4.1 Source of the images page 96 4.4.2 Problems with source data page 97 4.4.3 Obtaining homogeneous source data page 98 4.4.4 Pre-processing the source images page 99 4.4.4.1 Fourier analysis page 100 4.4.4.2 Processing frequency information page 102 4.5 Summary page 103 Chapter 5. Methodologies page 104 5.1 Introduction page 104 5.2 Back-propagation Networks page 104 5.2.1 The back-propagation algorithm page 104 5.2.1.1 Variations on vanilla page 106 5.3 Training the Network page 107 5.3.1 Selecting training and test sets page 107 5.3.2 Training the network page 109 5.3.3 Network mamrity; error, weights and epochs page 110 53.3.1 Error of a network page 110 533.2 Change in network weights page 112 53.3.3 Number of training epochs page 112 5.4 Testing the Network page 114 5.4.1 Multiple experimentation page 114 5.4.2 Correlation page 114 5.4.3 Extrapolation page 117 5.4.4 Regression analysis page 118 5.4.5 Probability measures of performance page 119 5.4.5.1 Generalisation page 119 5.45.2 Quality of generalisation page 121 5.453 Performance probabilities page 122 5.45.4 A proposed performance metric page 124 5.5 Summary page 125 Chapter 6. Initial Network Tests page 126 6.1 Introduction P^^S^ 6.2 Implementing Back-Propagation P^ge 126 6.2.1 Verifying the simulaUon page 126 6.2.i.I The Exclusive-Orfunction page 127 6.2.12 Simulating a network poge 127 6.2.1.3 Data compression Pi^S^^ ^30 6.3 Parameter Experiments page 131 6.3.1 Training set size experiment page 132 6.3.2 Training pattern size experiment page 133 6.3.3 Input size experiment page 134 6.3.4 Signal/noise ratioexpcriment page 135 6.3.5 Within- and between- class correlation page 136 6.4 Summary page 138 Chapter 7. Ceratium Trials page 139 7.1 Introduction P^ge 139 7.2 Ceratium Learning Trials page 139 7.2.1 Raw frequency experiments page 139 7.2.1.1 Raw frequency data P^S^ 7.2./.2 Raw frequency results P^^g^ 7.2.2 Frequency gradient experimenis page 143 7.2.2./ Frequency gradient page 143 7.2.2.2 Frequency gradient results page 144 7.3 Network Generalisation page 146 7.3.1 Classification in the normal condition page 146 7J.L1 Normal results page 146 7.3.2 Pattern noise experiments page 148 7.3.2.1 Additive pattern noise page 148 7.3.2.2 Additive pattern noise results page 149 7.3.3 Random pattern sequence presentation page !51 7.3.3.1 Training set order page 15 / 7. J.J.2 Training set order results page 151 7.3.4 Comb'ming pattern noise and sequence order page 152 7.J.4./ Order and noise results page 152 7.3.5 Limiting receptive fields page 153 7.3.5.1 Receptive fields page 153 7.3.5.2 Receptive fields results page 154 7.4 Summary page 155 Chapter 8. Network Analysis page 156 8.1 Introduction page 156 8.2 Solution Analysis page 156 8.2.1 Assessing generalisation page 156 8.2.2 Performance characteristics page 158 8.2.3 Analysis of network solutions page 162 8.2.4 Feature detection page 163 8.2.4.1 Fully-connected networks page 163 8.2.4.2 Limited receptive fields page 164 8.2.5 Hidden node discrimination page 165 8.2.6 Weight symmetry page 167 8.2.7 Arbitration of multiple solutions page 168 8.2.8 Nearest Neighbour trials ^_P?I^^^' 8.3 Summary-.!.... .-. — rr.T.T :. .7 page 173 Chapter 9. Discussion page 175 9.1 Introduction page 175 9.2 Ceratia Classification page 176 9.2.1 Applying neural networks page 176 9.2.2 Network classification pags 177 9.2.3 TTie human case page 178 9.2.4 Assessing network success page 179 9.2.5 Networks versus Neighbours page 181 9.3 Network Observation page 182 9.3.1 Network observaUon page 182 9.4 Improving Performance page 183 9.4.1 Selecting training data page 183 9.4.2 Arbitration page 184 9.4.3 Default classes page 184 9.4.4 Building lai^e classifiers page 185 9.5 Conclusion page 186 Appendix I - Pop-11 code page 188 Appendix II - Plankton specimen images and task instmction sheet page 195 References page 215 List of Illustrations and Tables.
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