Master thesis Computer science: Deep learning-based approach for water crystal classification

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Depending on the origin of the water and the formation process, crystals are divided into three main types: snow crystals, ice crystals, and water crystals. From the shape of the crystal, the purity and the texture level are clearly reflected, then it enables us to assess the quality of the water.

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Nội dung Text: Master thesis Computer science: Deep learning-based approach for water crystal classification

VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY DOAN THI HIEN DEEP LEARNING-BASED APPROACH FOR WATER CRYSTAL CLASSIFICATION MASTER THESIS Major: Computer Science HA NOI - 2021
Abstract Almost the earth’s surface area is covered by water. As it is pointed out in the 2020 edition of the World Water Development Report, climate change challenges the sustain- ability of water resources. It is important to monitor the quality of water to preserve sustainable water resources. Quality of water can be related to the water crystal struc- ture, solid-state of water, methods to understand water crystal help to improve water quality. First step, water crystal exploratory analysis has been initiated under cooper- ation with the Emoto Peace Project (EPP). The 5K EPP Dataset has been created as the first world-wide small dataset of water crystals. Our research focused on reducing inherent limitations when fitting machine learning models to the 5K EPP dataset. One major result is the classification of water crystals and how to split our small dataset into most related groups. Using the 5K EPP dataset human observations and past researches on snow crystal classification, we provided a simple set of visual labels to name water crystal shapes, with 12 categories. A deep learning-based method has been used to auto- matically do the classification task with a subset of the labeled dataset. The classification achieved high accuracy when fine-tuning the ResNet pretrained model. Keywords: Water crystal, Deep learning, Fine-tuning, Supervised, Classification. iii
Acknowledgements I would first like to thank my thesis supervisor Dr. Tran Quoc Long, Head of the Depart- ment of Computer Science at the University of Engineering and Technology. Thanks for his insightful comments both in my work and in this thesis, for his support, and many motivating discussions. I also want to acknowledge my co-supervisor Dr. Frederic Andres from the Na- tional Institute of Informatics, Japan for offering me the internship opportunities at NII, Japan and leading me working on diverse exciting projects. Without his support and experience, I could not achieve today result. Besides, I have been very privileged to get to know and to collaborate with many other great collaborators. Finally, I must express my very profound gratitude to my family for providing me with unfailing support and continuous encouragement throughout my years of study and through the process of researching and writing this thesis. This accomplishment would not have been possible without them. iv
Declaration I declare that the thesis has been composed by myself and that the work has not be submitted for any other degree or professional qualification. I confirm that the work submitted is my own, except where work which has formed part of jointly-authored publications has been included. My contribution and those of the other authors to this work have been explicitly indicated below. I confirm that appropriate credit has been given within this thesis where reference has been made to the work of others. This study was conceived by all of the authors. I carried out the main idea(s) and implemented all the model(s) and material(s). I certify that, to the best of my knowledge, my thesis does not infringe upon any- one’s copyright nor violate any proprietary rights and that any ideas, techniques, quota- tions, or any other material from the work of other people included in my thesis, pub- lished or otherwise, are fully acknowledged in accordance with the standard referencing practices. Furthermore, to the extent that I have included copyrighted material, I certify that I have obtained a written permission from the copyright owner(s) to include such material(s) in my thesis and have fully authorship to improve these materials. Master student Doan Thi Hien v
Table of Contents Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv Declaration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Difficulties and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Common Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.5 Contributions and Structure of the Thesis . . . . . . . . . . . . . . . . . . 6 2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.1 Manually Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Deep Learning-Based Approaches . . . . . . . . . . . . . . . . . . . . . . 9 3 The 5K EPP dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1 Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2 Water crystal definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.1 Theoretical Basis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.1.1 Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . 17 vi
4.1.2 Convolutional Autoencoder . . . . . . . . . . . . . . . . . . . . . . 19 4.1.3 Residual Connection . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.2 Overview of Proposed System . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.3 Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.3.1 Residual Autoencoder Model . . . . . . . . . . . . . . . . . . . . . 21 4.3.2 K-means algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.4 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.5 Data processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.5.1 Background removing . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.5.2 Dataset diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.5.3 Imbalanced data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 5 Experiments and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 5.1 Implementation and Configurations . . . . . . . . . . . . . . . . . . . . . . 29 5.1.1 Model Implementation . . . . . . . . . . . . . . . . . . . . . . . . . 29 5.1.2 Training and Testing Environment . . . . . . . . . . . . . . . . . . 30 5.2 Datasets and Evaluation methods . . . . . . . . . . . . . . . . . . . . . . . 31 5.2.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 5.2.2 Metrics and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . 32 5.3 Performance of Proposed model . . . . . . . . . . . . . . . . . . . . . . . . 33 5.3.1 Residual Autoencoder model (RAE) . . . . . . . . . . . . . . . . . 33 5.3.2 K-means for Clustering . . . . . . . . . . . . . . . . . . . . . . . . 35 5.3.3 Training Classification Model . . . . . . . . . . . . . . . . . . . . . 36 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 vii
Acronyms 2D 2-Dimensional 3D 3-Dimensional Adam Adaptive Moment Estimation AI Artificial Intelligence BCE Binary Cross Entropy CAE Convolutional Auto Encoder CNN Convolutional Neural Network CPU Central Processing Unit DNN Deep Neural Network EPP Emoto Peace Project FC Fully Connected GPU Graphics Processing Unit ILSVRC ImageNet Large Scale Visual Recognition Challenge MASC Multi-Angle Snowflake Camera MLP Multilayer Perceptron RAE Residual Auto Encoder viii
ReLU Rectified Linear Unit RNN Recurrent Neural Network SGD Stochastic Gradient Descent SSIM Structural Similarity Index ix
List of Figures 1.1 A typical pipeline of classification system . . . . . . . . . . . . . . . . . . 3 3.1 A tree-like diagram to demonstrate the water crystal categories with 5K EPP dataset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.1 System overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.2 Residual block’s structures. (a) The regular block. (b) The downsample block . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.3 Residual Autoencoder model to extract features from origin images. Each residual block is a combination of a downsample block and a regular block respectively. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.4 Clustering with K-means. The image features are extracted by Residual Autoencoder (RAE) model. Those features are then fed into the k-mean algorithm to do the clustering. . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.5 Otsu’s method is applied to find an object mask and remove the bac- ground which is not relevant to object area. . . . . . . . . . . . . . . . . . 26 5.1 Reconstruct image generated by RAE model train with BCE and Spher- ical metric separately. The SSIM index is calculated with each recon- structed image. The spherical one is outperforming the BCE one. . . . . . 34 5.2 A visualization for K-means clustering result. The number of classes which equals to 13 shows the best performance, with the densest space. . . 35 5.3 Three different transfer learning techniques were used to train the base- line model (SqueezeNet): feature extracting, fine-tuning, and proposed fine-tuning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 5.4 Our proposed model is compared with Hicks’s model. Both implementa- tions are trained on the 5K EPP dataset. Our accuracy (99.05%) is 0.2% higher than Hicks’ one (98.80%). . . . . . . . . . . . . . . . . . . . . . . . 39 x
List of Tables 3.1 The definition for water crystal classes based on the knowledge from [16] classification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 5K EPP dataset summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 5.1 Grid5000: Gemini clusters’s configuration. . . . . . . . . . . . . . . . . . 30 5.2 Statistics of 5K EPP dataset distribution used in the training classification model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 5.3 Top-1 Accuracy and F1 -score on 5K EPP dataset. . . . . . . . . . . . . . . 38 xi
Chapter 1 Introduction 1.1 Motivation Along with the development of society, the research on human impact on nature is more and more concerned. Water quality [4] has become one of the main challenges that societies will face during the 21st century, as the United Nations brought water quality issues to the forefront of international actions under the Sustainable Development Goal 6. It is important to monitor how human actions will affect water quality, pollution issues... Water has been playing an important role in the climatic ecosystem. Because most of our planet is covered by water, 70 to 90% of the human body (depending on age) is water. Testing the quality is simple, but not too simple as water can exist in different states or phases (liquid, solid, and gas). Advanced researches [8] has been done to understand water phases finding a new phase for the water liquid. Water quality can be evaluated in each of the four phases. Crystals are formed when water changes to a solid-state, are usually frozen at -25 to -30 degrees Celsius. Depending on the origin of the water and the formation process, crystals are divided into three main types: snow crystals, ice crystals, and water crystals. From the shape of the crystal, the purity and the texture level are clearly reflected, then it enables us to assess the quality of the water. Depending on the environmental conditions and the impact of the surrounding elements, the same water can give many different shapes. Each type of shape of crystals can be considered to be unique, without repetition. Up to now, a lot of research has been done to classify water solid form: crystals. Based on the researcher’s knowledge and available dataset, they focused on classifying the snowflake and ice crystals. A full definition of snowflake categories was proposed 1
and finalized overtime. But no research has been done with water crystals. While co-operating with Emoto Peace Project, we have a chance to work with the water crystal data, which is contributed over 20 years. We, therefore, have the urge to build a system to classify water crystal based on deep learning methods. We are interested in applying a deep learning model to extract the high-meaning features from 2D water crystal images then use those features to classify their structures. In this thesis, we focus on the 2 main tasks: (1) provide a new definition of water crystal structure and (2) build a classifier to split the labeled dataset into small groups. 1.2 Problem Statement Nowadays, the problem of environmental pollution is very concerned, especially water pollution. Along with the speed of development and urbanization in Vietnam, the prob- lem of water pollution is also becoming more and more serious. At the Workshop ”Wa- ter security for sustainable development in Vietnam” organized by the Vietnam Union of Science and Technology Associations (VUSTA), experts raised alarm about the state of water security in Vietnam. Currently 20% of people do not have access to clean water, 17.2 million people still use water sources that do not meet the clean water standards of the Ministry of Health. We decided to do this research to solve current two big problems related to water quality. The first problem is how can we assess the water quality. In fact, to check the qual- ity of water, we need to test according to many factors: Physical examination, Chemical test and Bacterial examination [4]. All those process take time and costs. The question is how to reduce costs and speed up the evaluation process. The second problem is how can we apply machine learning in water quality assess- ment. It’s mentioned as classification problem in machine learning. In the terminology of machine learning [1], classification is considered an in‘stance of supervised learning, in which the computer program learns from the data given to it and make new observa- tions or classifications. The main goal is to identify which class/category the new data will fall into. It can be performed on both structured or unstructured data. The process starts with predicting the class of given data points. The classes are often referred to as target, label, or categories. Figure 1.1 shows an overview of the classification system. While the classifier is trained with labeled data, it will be able to predict the class or 2
Figure 1.1: A typical pipeline of classification system category for the new data, which is kept secret with the classifier. Based on the data observation and machine learning-based knowledge, we focus on building an deep learning model to classify the water crystal, in which we can assess the water quality. To build a machine learning classification system, it requires two main parts: data and algorithm. • Data is the most important part to build any machine learning system. It can be a set of observations or instances, which are correctly labeled by humans or a trust system. Data should be present in a numeric vector or matrix. For example, an image X is presented as a matrix of real values " where each #number is a pixel, 0.1 122.5 255.0 demonstrate for an image illumination: X = 0.1 255.0 255.0 • Algorithm is a mapping function from input variables to output variables. Given a dataset X , algorithm f is responsible for mapping X to a specific class y : y = f (X). An algorithm that implements classification, especially in a concrete implementa- tion, is known as a classifier. The term “classifier” sometimes also refers to the mathematical function, implemented by a classification algorithm, that maps input data to a category. In this thesis, we mainly focus on building a deep learning-based classification and providing a high-quality dataset with water crystal to teach the algorithm the mapping function. 3
1.3 Difficulties and Challenges Classification is a general problem in computer science. Even we have many proposed works in these tasks, we need to build a new classifier for each specific domain. There exists some of difficulties and challenges, from the basic issue of deep learning classifi- cation to its various specific issues as below: • Small dataset. With the specific condition of surrounding, the same water bottle can form different WCs. Any small change can lead to a different and unexpected type of crystal. Therefore, while getting a water sample and capturing the photo from it, the scientist needs to do it very carefully. Besides, to enhance the diversity of the dataset, the scientist needs to collect dataset all around the world with the help of other organization. • Imbalanced data. It is considered as an extremely serious classification issue, in which we can expect poor accuracy for minor classes. Generally, only positive instances are annotated in most relation extraction corpora, so negative instances must be generated automatically by pairing all the entities appearing in the same sentence that has not been annotated as positives yet. Because of a big number in such entities, the number of possible negatives pairs is huge. • High resolution image. To capture the water crystal, the Emoto laboratory used the microscope camera. Because the crystals are very small, and to capture full details, they need to keep the highest resolution. So, the final dataset has a very high resolution, which is considered as a serious issue with deep learning. There are many other difficulties in applying deep learning in the domain of water crystal. The main constraints is Lack of training data. To train a deep learning model, it requires a large size dataset with good quality. In general image classification problems, training dataset can be download easily from the internet with good quality and quantity (i.e. ImageNet, MNIST, satellite imagery, etc.). However, with water crystal, there are many limitations on collecting dataset from many countries and resources to get a diverse dataset. Therefore, it is hard to enrich the dataset. Besides, it is time and money consuming for labeling because it requires special experts with domain knowledge. However, none of the current approaches can solve these problems. Therefore, special approaches are required to archive good results. 4
1.4 Common Approaches From 1931, when Wilson Bentley created the first method of photographing snowflakes, much research has been done in the classification tasks. The most popular approach to classify crystal is manually classification, which is based on human observation. In recent years, with the advent of deep learning, deep learning-based classification was proposed. All of them are proven to be effective and have different strengths by leveraging different types of linguistic knowledge, however, also suffer from their own limitations. From a physical point of view, Ta − s diagram (Nakaya’s classification) was pro- posed to classify snow crystals which are collected from Mount Takachi [23]. This method is very simple and cannot be used for the irregular form of snow crystals, the most popular form in nature. An improvement version of Nakaya’s classification was proposed [22]. This method can describe the meteorological difference in the group of asymmetric or modified types of snow crystals. However, the snow crystals were col- lected in a specific area. So, it reduces the diversity of the dataset. To overcome this problem, a global classification was made to classify snow crystal, ice crystal, and solid precipitation particles [16]. These observations were done from middle latitudes (Japan) to polar regions. However, this classification takes time to classify with a large-scale dataset. Another approach using a deep learning-based method was proposed in recent years. With the images collected by Multi-Angle Snowflake Camera (MASC) [7], some research using a deep learning method was proposed. With supervised learning, a com- bination of convolutional neural network and residual network which pretrained with ImageNet was used as a backbone for the classification model. This method provides geometrics and the degrees of rimming classification. Another unsupervised learning method was published to overcome the problem with the large-scale dataset and human intervention. GAN and K-medoids are used to classify MASC dataset into 16 hierarchi- cal clustering groups. Even that it can automatically classification the snowflakes type, but this model just fits with the MASC snowflake dataset only. The detail and overview of related work will be stated in Chapter 2. 5
1.5 Contributions and Structure of the Thesis Up to now, working with a natural material like water still attract the interesting of many researchers in the world. Especially with classification problems, not many deep learning-based methods are applied to this problem. In our knowledge, most previous researches often focus on classifying water crystals based on their knowledge base or human knowledge. Considering these problems as motivation to improve, in this paper, we present a deep learning-based method to solve that problem. In this work, we focus on building a basic definition based on EPP dataset. Based on that definition, a deep learning model is used to automatically classify the labeled dataset. Consider the limitations of a small and imbalanced dataset, we analyze the results and fine-tune the parameters after each training stage. The main contributions of our work can be concluded as: • We proposed a new definition for water crystal structure based on previous related research, especially in [16]. This definition can be known as the first one in water crystal classification. • We introduce a new data science dataset in water crystal structures, which was collected by Emoto laboratory and labeled based on our new definition. We named that it 5K EPP dataset. • We proposed an end-to-end trainable model to extract meaningful features from a high-resolution water crystal dataset. The model is inspired by the Autoencoder model and residual neural network. • We proposed a deep learning model to classify the 5K EPP dataset. We overcome the problem when training the model with a small and imbalanced dataset. We also make a comparison between multiple deep learning networks and find the best solution. My thesis includes five main Chapters and one Conclusions, as follow: Chapter 1: Introduction. This Chapter is an introduction to the water crystal problem, an overview of common approaches to classification problems. We present the motivations and the difficulties and challenges of Relation Extraction as well. Chapter 2: Related Work. We introduce relevant related work shared among all the methods in this thesis. This chapter introduces the history and development of 6
crystal dataset and related research on classification tasks, from the traditional methods to the deep learning method. Chapter 3: The 5K EPP dataset. The 5K EPP dataset is described in this Chapter. A fully description text and diagram are provided in this Chapter for a better understand- ing of our new definition. Chapter 4: Materials and Methods. Chapter 4 begins by providing an overview of our deep learning background used in this thesis. Next, we will introduce how we build the Residual Autoencoder model to extract features from the EPP dataset. Then, we present the classifier overview architecture. Finally, we conclude the chapter by providing a brief introduction to how we improve our model’s performance with several techniques. Chapter 5: Experiments and Results. We provide an insight into the implemen- tation of the models and discuss the hyper-parameter settings. Next, we evaluate our model on the 5K EPP dataset with different backbone. The method introduced in Chap- ter 4 are compared to find the best architecture with this dataset. Finally, we analyze the output and the error for better insight into our models. Conclusions. This chapter concludes the thesis by summarizing the important contributions and results. Also, we highlight the limitations of our models and point out some further extensions in the future work. 7
Chapter 2 Related Work With a research focus to improve precipitation measurement and forecast for over 50 years, the scientific study of meteorology and weather includes the study of snowflakes, ice crystals, and water crystals. Snowflake studies provide some of the most detailed evidence of climate change. It impacts atmospheric science. We categorize approaches to crystals classification into two main categories: Manually Approaches (Section 2.1) and Deep Learning-Based Approaches (Section 2.2). 2.1 Manually Approaches One of the first attempts to catalog snowflakes was made in the 1930s by Wilson Bentley who created a method of photographing snowflakes in 1931, using a microscope attached to a camera. The Bentley Snow Crystal Collection [3] includes about 6125 items. A general classification of snow crystals Ta − s diagram was proposed by Nakaya [23], which provides the most perfect classification from a physical point of view, with 7 categories. These categories include needles, columns, fern-like crystals developed in one plane, combination of column and plane crystals, rimed crystals, and irregular crystals. The crystal images were collected from a slope of Mount Takachi, near the center of Hokkaido Island. Magono [22] published an improvement version of Nakaya’s classification, with the modification and supplement for Nakaya’s classification of snow crystals. The re- sults got by laboratory experiments and meteorological observation. The new classifi- cation provides the temperature and humidity conditions, which can describe the mete- orological difference in the group of asymmetric or modified types of snow crystals. It 8
provides 80 categories, which has some modification from Nakaya’s categories and add some new categories. Thirty thousand microscopic photographs of snow crystals taken by the Cloud Physics Group were used in their research. Kikuchi and his team [16] proposed a new classification with 121 categories to classify snow crystal, ice crystal, and solid precipitation particles. They qualified its classification ”global scale” or ”global” because their observations were done from mid- dle latitudes (Japan) to polar regions. This classification consisted of three levels: gen- eral, intermediate, and elementary - which are composed of 8, 39, and 121 categories, respectively. Especially, this classification can be used not only for snow crystals but also for ice crystals. Radin et al. published two studies related to the effects of distant intention on water crystal formation [27, 28]. In these research, they did the experiments on how a group of people’s intentions can affect the water samples located inside a far-away laboratory. They put the positive intentions to all the samples, send the water bottles to Emoto Laboratory in Tokyo to get the crystals from them. A double and triple-blind test was done respectively. 2.2 Deep Learning-Based Approaches The deep learning method has been widely applied in many research fields, especially with image dataset. But it faces the problem of the dataset’s limitation. Fortunately, with the advent of image collection methods, a method to collect snowflake images was proposed: the Multi-Angle Snowflake Camera (MASC) [7]. It was developed to address the need for high-resolution multi-angle imaging of hydrometeors in freefall and has resulted in datasets comprising millions of images of falling snowflakes. Therefore, there is many research have been published. A new method to automatically classify solid hydrometeors based on MASC im- ages is presented by Praz et al. [26]. In this research, they proposed a regularized multinomial logistic regression (MLR) model to output the probabilistic information of MASC images. That probability is then weighed on the three stereoscopic views of the MASC to assign a unique label to each hydrometeor. MLR model is trained over more than 3,000 MASC images labeled by visual inspection. This model achieved very high performance with 95% accuracy. Hicks et al. [12] published an automatic method to classify snowflakes, collected 9