UNIVERSITY of CALIFORNIA SAN DIEGO Improving Biological Object Classification in Plankton Images Using Convolutional Neural
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UNIVERSITY OF CALIFORNIA SAN DIEGO Improving Biological Object Classification in Plankton Images Using Convolutional Neural Networks, Geometric Features, and Context Metadata A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Computer Science by Jeffrey Scott Ellen Committee in charge: Professor Charles Elkan, Co-Chair Professor Mark D. Ohman, Co-Chair Professor Virginia R. de Sa Professor Lawrence K. Saul Professor Zhuowen Tu 2018 SIGNATURE PAGE The dissertation of Jeffrey Scott Ellen is approved, and it is acceptable in quality and form for publication on microfilm and electronically: __________________________________________________________________ __________________________________________________________________ __________________________________________________________________ __________________________________________________________________ Co-Chair __________________________________________________________________ Co-Chair University of California San Diego 2018 iii DEDICATION To my family for all their support, especially: my loving wife, two patient children, and my parents. iv EPIGRAPH “Education is what remains after one has forgotten what one has learned in school.” Albert Einstein “Don’t Panic” Douglas Adams; The Hitchhiker’s Guide to the Galaxy v TABLE OF CONTENTS SIGNATURE PAGE ......................................................................................................... iii DEDICATION .................................................................................................................. iv EPIGRAPH ..........................................................................................................................v TABLE OF CONTENTS ................................................................................................... vi LIST OF FIGURES .............................................................................................................x LIST OF TABLES ......................................................................................................... xviii ACKNOWLEDGEMENTS ............................................................................................. xix VITA ............................................................................................................................ xxvi ABSTRACT OF THE DISSERTATION ..................................................................... xxvii CHAPTER 1 Introduction to the Dissertation ..................................................................1 1.1 The Case for Automating Biological Object Classification ..........................2 1.2 Defining the Problem Space ..........................................................................3 1.3 Motivating Questions .....................................................................................6 1.3.1 What is the prior state-of-the-art for plankton classification in images? ................................................................................................6 1.3.2 What is the improvement from using contemporary convolutional neural networks? ..................................................................................7 1.3.3 Can context metadata be used to improve classification accuracy? ....9 1.4 Outline of the Dissertation ...........................................................................10 1.5 References ....................................................................................................16 CHAPTER 2 A Review of Feature Extraction Techniques for Automating Biological Object Classification in Images .............................................................................20 2.1 Introduction ..................................................................................................21 2.2 Review Organization ...................................................................................21 2.3 Other Feature Extraction Reviews ...............................................................23 2.4 Statistical Analysis Methods. .......................................................................26 2.4.1 Moment Based Methods ....................................................................26 2.4.2 Histogram Based Methods .................................................................29 2.4.3 Texture Based Methods .....................................................................30 2.5 Statistical Analysis Methods Specifically for Biological Object Classification ............................................................................................................31 2.6 Topology Based Methods ............................................................................38 vi 2.6.1 Boundary Matching Methods ............................................................39 2.6.2 Path Matching Methods .....................................................................43 2.6.3 Skeleton Matching Methods ..............................................................48 2.7 Topology Based Methods Specifically for Biological Object Classification ..52 2.8 Point/Patch Correspondence Methods .........................................................59 2.8.1 Fixed Heuristics .................................................................................59 2.8.2 Point Correspondence Based Methods ..............................................60 2.8.3 Patch/Filter Based Methods ...............................................................62 2.9 Point/Patch Correspondence Methods Specifically for Biological Object Classification.............................................................................................................66 2.10 Discussion ....................................................................................................70 2.10.1 Algorithm Tuning ..............................................................................70 2.10.2 Ensemble Methods .............................................................................71 2.10.3 Deep Learning ....................................................................................72 2.11 Conclusion ...................................................................................................73 2.12 Acknowledgements ......................................................................................75 2.13 References ....................................................................................................76 CHAPTER 3 Improving Object Detection and Segmentation for In Situ Plankton Images ..................................................................................................................84 3.1 Introduction ..................................................................................................85 3.2 Prior Image Processing Techniques Extended for Zooglider Images ..........86 3.2.1 Flat-fielding of scientific images .......................................................86 3.2.2 Segmentation of plankton images ......................................................87 3.2.3 Embedding Metadata with the Extensible Metadata Platform (XMP) format .................................................................................................91 3.3 Original Image Correction and Segmentation Algorithms ..........................92 3.3.1 Acquisition and Characterization of Zooglider images .....................92 3.3.2 Flat-fielding of Zooglider Images ......................................................93 3.3.3 Segmentation of Zooglider Images ....................................................96 3.3.4 Embedding Metadata as XMP .........................................................101 3.4 Results ........................................................................................................102 3.4.1 Flat-fielding Successes and Limitations ..........................................102 3.4.2 Segmentation Successes and Limitations ........................................105 vii 3.5 Summary ....................................................................................................113 3.6 Acknowledgements ....................................................................................115 3.7 References ..................................................................................................115 CHAPTER 4 Quantifying California Current Plankton Samples with Efficient Machine Learning Techniques .............................................................................118 4.1 Introduction ................................................................................................119 4.2 Machine Learning Experimentation ..........................................................119 4.3 Experimental Results .................................................................................121 4.4 Conclusion .................................................................................................125 4.5 Appendix ....................................................................................................126 4.6 References ..................................................................................................126 CHAPTER 5 Correlating Filter Diversity with Convolutional Neural Network Accuracy ..............................................................................................................128 5.1 Introduction ................................................................................................129 5.2 Experimental Dsign ...................................................................................129