Nghiên cứu phát triển các phương pháp của lý thuyết đồ thị và otomat trong giấu tin mật và mã hóa tìm kiếm

HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY NGUYEN THI THANH NHAN INTERACTIVE AND MULTI-ORGAN BASED PLANT SPECIES IDENTIFICATION Major: Computer Science Code: 9480101 INTERACTIVE AND MULTI-ORGAN BASED PLANT SPECIES IDENTIFICATION SUPERVISORS: Assoc Prof Dr Le Thi Lan Prof Dr Hoang Van Sam Hanoi − 2020 HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY Nguyen Thi Thanh Nhan INTERACTIVE AND MULTI-ORGAN BASED PLANT SPECIES IDENTIFICATION Major: Computer Science Code: 9480101 DOCTORAL DISSERTATION OF COMPUTER SCIENCE SUPERVISORS: Assoc Prof Dr Le Thi Lan Prof Dr Hoang Van Sam Hanoi − 2020 DECLARATION OF AUTHORSHIP I, Nguyen Thi Thanh Nhan, declare that this dissertation entitled, ”Interactive and multi-organ based plant species identification”, and the work presented in it is my own I confirm that: This work was done wholly or mainly while in candidature for a Ph.D research degree at Hanoi University of Science and Technology Where any part of this dissertation has previously been submitted for a degree or any other qualification at Hanoi University of Science and Technology or any other institution, this has been clearly stated Where I have consulted the published work of others, this is always clearly attributed Where I have quoted from the work of others, the source is always given With the exception of such quotations, this dissertation is entirely my own work I have acknowledged all main sources of help Where the dissertation is based on work done by myself jointly with others, I have made exactly what was done by others and what I have contributed myself Hanoi, May, 2020 PhD Student Nguyen Thi Thanh Nhan SUPERVISORS i ACKNOWLEDGEMENT First of all, I would like to thank my supervisors Assoc Prof Dr Le Thi Lan at The International Research Institute MICA - Hanoi University of Science and Technology, Assoc Prof Dr Hoang Van Sam at Vietnam National University of Forestry for their inspiration, guidance, and advice Their guidance helped me all the time of research and writing this dissertation Besides my advisors, I would like to thank Assoc Prof Dr Vu Hai, Assoc Prof Dr Tran Thi Thanh Hai for their great discussion Special thanks to my friends/colleagues in MICA, Hanoi University of Science and Technology: Hoang Van Nam, Nguyen Hong Quan, Nguyen Van Toi, Duong Nam Duong, Le Van Tuan, Nguyen Huy Hoang, Do Thanh Binh for their technical supports They have assisted me a lot in my research process as well as they are co-authored in the published papers Moreover, I would like to thank reviewers of scientific conferences, journals and protection council, reviewers, they help me with many useful comments I would like to express a since gratitude to the Management Board of MICA Institute I would like to thank the Thai Nguyen University of Information and Communication Technology, Thai Nguyen over the years both at my career work and outside of the work As a Ph.D student of the 911 program, I would like to thank this program for financial support I also gratefully acknowledge the financial support for attending the conferences from the Collaborative Research Program for Common Regional Issue (CRC) funded by ASEAN University Network (Aun-Seed/Net), under the grant reference HUST/CRC/1501 and NAFOSTED (grant number 106.06-2018.23) Special thanks to my family, to my parents-in-law who took care of my family and created favorable conditions for me to study I also would like to thank my beloved husband and children for everything they supported and encouraged me for a long time to study Hanoi, May, 2020 Ph.D Student Nguyen Thi Thanh Nhan ii CONTENTS DECLARATION OF AUTHORSHIP i ACKNOWLEDGEMENT ii CONTENTS v SYMBOLS vi SYMBOLS viii LIST OF TABLES xi LIST OF FIGURES xvi INTRODUCTION 1 LITERATURE REVIEW 1.1 Plant identification 1.1.1 Manual plant identification 1.1.2 Plant identification based on semi-automatic graphic tool 1.1.3 Automated plant identification 1.2 Automatic plant identification from images of single organ 1.2.1 Introducing the plant organs 1.2.2 General model of image-based plant identification 1.2.3 Preprocessing techniques for images of plant 1.2.4 Feature extraction 1.2.4.1 Hand-designed features 1.2.4.2 Deeply-learned features 1.2.5 Training methods 1.3 Plant identification from images of multiple organs 1.3.1 Early fusion techniques for plant identification from images multiple organs 1.3.2 Late fusion techniques for plant identification from images multiple organs 1.4 Plant identification studies in Vietnam 1.5 Plant data collection and identification systems 1.6 Conclusions iii of of 10 10 10 11 12 13 13 16 17 19 20 22 25 28 30 31 33 35 43 LEAF-BASED PLANT IDENTIFICATION METHOD BASED ON KERNEL DESCRIPTOR 2.1 The framework of leaf-based plant identification method 2.2 Interactive segmentation 2.3 Feature extraction 2.3.1 Pixel-level features extraction 2.3.2 Patch-level features extraction 2.3.2.1 Generate a set of patches from an image with adaptive size 2.3.2.2 Compute patch-level feature 2.3.3 Image-level features extraction 2.3.4 Time complexity analysis 2.4 Classification 2.5 Experimental results 2.5.1 Datasets 2.5.1.1 ImageCLEF 2013 dataset 2.5.1.2 Flavia dataset 2.5.1.3 LifeCLEF 2015 dataset 2.5.2 Experimental results 2.5.2.1 Results on ImageCLEF 2013 dataset 2.5.2.2 Results on Flavia dataset 2.5.2.3 Results on LifeCLEF 2015 dataset 2.6 Conclusions FUSION SCHEMES FOR MULTI-ORGAN BASED PLANT IDENTIFICATION 3.1 Introduction 3.2 The proposed fusion scheme RHF 3.3 The choice of classification model for single organ plant identification 3.4 Experimental results 3.4.1 Dataset 3.4.2 Single organ plant identification results 3.4.3 Evaluation of the proposed fusion scheme in multi-organ plant identification 3.5 Conclusion TOWARDS BUILDING AN AUTOMATIC PLANT RETRIEVAL BASED ON PLANT IDENTIFICATION 4.1 Introduction 4.2 Challenges of building automatic plant identification systems iv 45 45 46 50 50 51 51 52 55 56 57 57 57 57 57 58 58 58 61 61 68 69 69 71 77 79 80 81 81 89 90 90 90 4.3 4.4 4.5 4.6 The framework for building automatic plant identification system 94 Plant organ detection 96 Case study: Development of image-based plant retrieval in VnMed application 101 Conclusions 106 CONCLUSIONS AND FUTURE WORKS 107 4.6.1 Short term 108 4.6.2 Long term 108 Bibliography 110 PUBLICATIONS 124 APPENDIX 125 v ABBREVIATIONS No Abbreviation Meaning AB Ada Boost ANN Artificial Neural Network Br Branch CBF Classification Base Fusion CNN Convolution Neural Network CNNs Convolution Neural Networks CPU Central Processing Unit CMC Cumulative Match Characteristic Curve DT Decision Tree 10 En Entire 11 FC Fully Connected 12 Fl Flower 13 FN False Negative 14 FP False Positive 15 GPU Graphics Processing Unit 16 GUI Graphic-User Interface 17 HOG Histogram of Oriented Gradients 18 ILSVRC ImageNet Large Scale Visual Recognition Competition 19 KDES Kernel DEScriptors 20 KNN K Nearest Neighbors 21 Le Leaf 22 L-SVM Linear Support Vector Machine 23 MCDCNN Multi Column Deep Convolutional Neural Networks 24 NB Naive Bayes 25 NNB Nearest NeighBor 26 OPENCV OPEN source Computer Vision Library 27 PC Persional Computer 28 PCA Principal Component Analysis 29 PNN Probabilistic Neural Network 30 QDA Quadratic Discriminant Analysis vi 31 RAM Random Acess Memory 32 ReLU Rectified Linear Unit 33 RHF Robust Hybrid Fusion 34 RF Random Forest 35 ROI Region Of Interest 36 SIFT Scale-Invariant Feature Transform 37 SM SoftMax 38 SURF Speeded Up Robust Features 39 SVM Support Vector Machine 40 SVM-RBF Support Vector Machine-Radial Basic Function kernel 41 TP True Positive 42 TN True Negative vii MATH SYMBOLS No Symbol Meaning % 1% = 1/100 × Multiplication + Addition Summation - sum of all values in range of series Multiplication R Set of real number Rd Set of real number has d dimensions / Division = Equality 10 ≥ Greater than or equal to 11 ≤ Less than or equal to 12 π π = 3.141592654 13 w L2 normalize of vector w 14 xi The i-th element of vector x 15 sign(x) The sign function that determines 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Thanh Nhan Nguyen, Van Tuan Le, Thi Lan Le, Hai Vu, Natapon Pantuwong and Yasushi Yagi (2016), Flower species identification using deep convolutional neural networks, AUN/SEED-Net Regional Conference on Computer and Information Engineering 2016, Yangon, Myanmar, ISBN: 978-99971-0-231-7, pp.51-56 [4] Thi Thanh-Nhan Nguyen, Thi-Lan Le, Hai Vu, Huy-hoang Nguyen and VanSam Hoang (2017), A combination of Deep Learning and Hand-Designed Feature for Plant Identification Based on Leaf and Flower, In Asian Conference on Intelligent Information and Database Systems, Studies in Computational Intelligence, volume 710, Springer, ISBN: 978-3-319-56659-7, pp 223-233 [5] Nguyen Thi Thanh Nhan, Do Thanh Binh, Nguyen Huy Hoang, Vu Hai, Tran Thi Thanh Hai, Thi-Lan Le (2018), Score-based Fusion Schemes for Plant Identification from Multi-organ Images, VNU Journal of Science: Computer Science and Communication Engineering, Vol 34, No.2, ISSN 2588-1086, pp.1-15 [6] Thi Thanh Nhan Nguyen, Thi-Lan Le, Hai Vu, Van-Sam Hoang, Thanh-Hai Tran (2018), Crowdsourcing for botanical data collection towards to automatic plant identification: A review, Journal of Computers and Electronics in Agriculture (SCIE), vol 155, ISSN:0168-1699, pp.412-425 [7] Nguyen Thi Thanh Nhan, Le Thi Lan, Vu Hai, Hoang Van Sam (2018), Automatic Plant Organ Detection from Images using Convolutional Neural Networks, Journal of Research and Development on Information and Communication Technology (in Vietnamese), vol V-1, No 39, ISSN: 1859-3526, pp 17-25 [8] Thi Thanh-Nhan Nguyen, Thi-Lan Le, Hai Vu, Van-Sam Hoang (2019), Towards an automatic plant identification system without dedicated dataset International Journal of Machie Learning and Computing (Scopus), vol 9, No.1, ISSN: 2010-3700, pp.26-34 APPENDIX A Table A.1 Details of 50 species used in chapter ID Species name #Leaf #Flower #Branch #Entire 54 Cotinus coggygria Scop 91 38 43 18 55 Pistacia lentiscus L 119 33 51 12 151 Daucus carota L 22 157 23 64 326 Ilex aquifolium L 103 55 49 23 329 Hedera helix L 244 46 58 44 333 Aristolochia clematitis L 40 31 46 26 505 Bidens pilosa L 39 43 35 24 702 Erigeron sumatrensis Retz 56 36 37 42 1212 Silybum marianum (L.) Gaertn 46 54 48 44 10 1321 Alnus glutinosa (L.) Gaertn 60 74 91 14 11 1325 Betula pendula Roth 88 34 64 38 12 1328 Carpinus betulus L 67 47 66 14 13 1329 Corylus avellana L 158 65 61 21 14 1837 Buddleja davidii Franch 39 77 47 24 15 1845 Opuntia ficus-indica (L.) Mill 64 55 23 29 16 1966 Lonicera xylosteum L 48 50 75 14 17 1968 Sambucus nigra L 74 78 37 20 18 1972 Viburnum opulus L 40 39 46 13 19 1973 Viburnum tinus L 179 109 64 16 20 2631 Diospyros kaki L.f 73 49 30 19 21 2648 Arbutus unedo L 58 84 72 24 22 2776 Amorpha fruticosa L 56 57 54 20 23 2841 Ceratonia siliqua L 49 29 50 31 24 2918 Gleditsia triacanthos L 40 30 87 16 25 3124 Robinia pseudoacacia L 89 90 56 20 26 3279 Castanea sativa Mill 52 55 66 17 27 3283 Quercus coccifera L 59 40 71 22 28 3288 Quercus ilex L 162 106 80 49 29 3435 Geranium robertianum L 33 80 44 55 125 30 3465 Aesculus hippocastanum L 81 105 51 26 31 3506 Juglans regia L 53 36 62 27 32 3750 Laurus nobilis L 156 84 78 17 33 3797 Liriodendron tulipifera L 82 40 48 42 34 3798 Magnolia grandiflora L 71 85 60 14 35 3801 Alcea rosea L 43 77 48 23 36 3831 Malva sylvestris L 63 200 80 32 37 3846 Broussonetia papyrifera (L.) Vent 76 39 104 17 38 3849 Morus alba L 104 19 52 113 39 3942 Fraxinus angustifolia Vahl 47 45 100 20 40 3956 Olea europaea L 92 32 47 41 41 4026 Chelidonium majus L 58 83 42 63 42 4074 Pittosporum tobira (Thunb.) W.T.Aiton 127 75 52 41 43 4094 Plantago lanceolata L 39 64 10 82 44 4109 Platanus x hispanica Mill ex Mă unchh 99 42 47 33 45 4111 Platanus orientalis L 57 27 35 21 46 4721 Crataegus monogyna Jacq 135 151 79 21 47 5522 Veronica persica Poir 14 114 56 60 48 14869 Ulex europaeus L 66 93 44 50 49 14887 Verbascum thapsus L 94 110 50 80 50 30131 Euphorbia amygdaloides L 65 47 55 65 3870 3309 2774 1661 Total 126 ... often have high distinguishing characteristics of the input image Extracting features can reduce the size of the information displayed in the image while features are highly distinctive Feature... images of the to-be-identified plant by directly capturing images in the field or selecting images in the existing albums Through this thesis, we use the following terminologies that are defined... are also described for future works CHAPTER LITERATURE REVIEW This chapter aims at presenting the existing works proposed for plant identification in the literature First, we introduce three