DOCSLIB.ORG

A Hybrid Autoencoder Network for Unsupervised Image Clustering

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Hybrid Autoencoder Network for Unsupervised Image Clustering algorithms Article A Hybrid Autoencoder Network for Unsupervised Image Clustering Pei-Yin Chen and Jih-Jeng Huang * Department of Computer Science & Information Management, SooChow University, No.56 Kueiyang Street, Section 1, Taipei 100, Taiwan; [email protected] * Correspondence: [email protected] Received: 29 April 2019; Accepted: 13 June 2019; Published: 15 June 2019 Abstract: Image clustering involves the process of mapping an archive image into a cluster such that the set of clusters has the same information. It is an important field of machine learning and computer vision. While traditional clustering methods, such as k-means or the agglomerative clustering method, have been widely used for the task of clustering, it is difficult for them to handle image data due to having no predefined distance metrics and high dimensionality. Recently, deep unsupervised feature learning methods, such as the autoencoder (AE), have been employed for image clustering with great success. However, each model has its specialty and advantages for image clustering. Hence, we combine three AE-based models—the convolutional autoencoder (CAE), adversarial autoencoder (AAE), and stacked autoencoder (SAE)—to form a hybrid autoencoder (BAE) model for image clustering. The MNIST and CIFAR-10 datasets are used to test the result of the proposed models and compare the results with others. The results of the clustering criteria indicate that the proposed models outperform others in the numerical experiment. Keywords: image clustering; convolutional autoencoder (CAE); adversarial autoencoder (AAE); stacked autoencoder (SAE) 1. Introduction Data mining (DM) is the one of the key processes of knowledge discovery in databases (KDD) to transform raw data into interesting information and knowledge [1] and is the main research topic in the field of machine learning and artificial intelligence. The classification of data mining depends on the tasks of the problem; one of the tasks is clustering, which groups similar data into a segment. Here, we focus on image clustering, which is an essential issue in machine learning and computer vision. Hence, the purpose of image clustering is to group images into clusters such that an image is similar to others within a cluster. Many statistical methods, e.g., k-means or DBSCAN, have been used in image clustering. However, these methods have difficulty in handling image data, since images are usually high-dimensional, resulting in the poor performance of these traditional methods [2]. Recently, with the development of deep learning, more neural network models have been developed for image clustering. The most famous one is the autoencoder (AE) network, which firstly pre-trains deep neural networks with unsupervised methods and employs traditional methods, e.g., k-means, for clustering images in post-processing. More recently, several autoencoder-based networks have been proposed—e.g., the convolutional autoencoder (CAE) [3], adversarial autoencoder (AAE) [4], stacked autoencoder (SAE) [5], variational autoencoder (VAE) [6,7], etc.—and these models have been reported to achieve great success in the fields of supervised and unsupervised learning [8–10]. However, as we know of no technique or model which outperforms others in all situations, every model has its specialty to specific tasks or functions. Therefore, although numerous autoencoder-based Algorithms 2019, 12, 122; doi:10.3390/a12060122 www.mdpi.com/journal/algorithms Algorithms 2019, 12, 122 2 of 11 models have been proposed to learn feature representations from images, the best one depends on the situation. Hence, in this paper, we consider a hybrid model, namely the hybrid autoencoder (BAE), to integrate the advantages of three autoencoders, CAE, AAE, and SAE, to learn low and high-level feature representation. Then, we use the k-means to cluster images. The concept of hybrid modelsAlgorithms is not a 2019 new, 12, idea.x FOR PEER For REVIEW example, [11] integrated AE and density estimation models2 of 11 to take advantage of their different strengths for anomaly detection. [12] proposed a hybrid spatial–temporal best one depends on the situation. Hence, in this paper, we consider a hybrid model, namely the autoencoderhybrid toautoencoder detect abnormal (BAE), to eventsintegrate in the videos advantages by the of three integration autoencoders, of a CAE, long AAE, short-term and SAE, memory (LSTM)to encoder–decoder learn low and high- andlevel thefeature convolutional representation. autoencoder. Then, we use the These k-means results to cluster of the images. hybrid The models outperformconcept other of hybrid state-of-the-art models is not models. a new idea. For example, [11] integrated AE and density estimation Themodels differences to take of advantage the proposed of their method different from strengths others for are anomaly that first detection. we focus [12] onproposed the task a hybrid of clustering, and thenspatial we integrate–temporal di autoencoderfferent AE-based to detect methods, abnormal which events were in videos not considered by the integration previously. of a Inlong addition, short-term memory (LSTM) encoder–decoder and the convolutional autoencoder. These results of in our experiment, we use two datasets, MNIST and CIFAR-10, which are famous image datasets, the hybrid models outperform other state-of-the-art models. to compareT thehe clusteringdifferences of performance the proposed withmethod others. from Theothers experiment are that first results we focus indicate on the thetask clustering of performanceclustering of,the and proposed then we methodintegrate isdifferent better AE than-based that methods of others, which with were respect not toconsider the resultsed of unsupervisedpreviously. clustering In addition, accuracy in our (ACC),experiment, normalized we use two mutual datasets information, MNIST and (NMI),CIFAR-10, and which adjusted are rand index (ARI).famous image datasets, to compare the clustering performance with others. The experiment results indicate the clustering performance of the proposed method is better than that of others with respect 2. Autoencoder-Basedto the results of unsupervised Networksfor clustering Clustering accuracy (ACC), normalized mutual information (NMI), and adjusted rand index (ARI). An AE is a neural network which is trained to reconstruct the input from the hidden layer. The main2. Autoencoder characteristic-Based of Ne antworks AE is for the Clustering encoder–decoder network, which is used to train for the representationAn AE code. is a neural The representation network which is code trained usually to reconstruct has a smallerthe input dimensionalityfrom the hidden layer. than The the input layer, canmain be characteristic considered asof thean AE compressed is the encoder feature–decoder representation network, which of the is originalused to trai variablesn for the and can be usedrepresentation for further data code. mining The representation tasks, e.g., dimensioncode usually reductionhas a small [er13 dimensionality,14], classification than the and input regression layer, can be considered as the compressed feature representation of the original variables and can models [15,16], and clustering analysis [8,12]. In addition, the feature representation from an AE can be used for further data mining tasks, e.g., dimension reduction [13,14], classification and regression presentmodels high-level [15,16] features, and clustering rather analysis than the [8,12] low-level. In addition, features the feature which representation are only derived from anby AEtraditional can methods,present e.g., high HOG-level [17 features] or SIFT rather [18 than], and the low may-level suff featureser from which appearance are only derived variations by tradition of scenesal and objects [methods,2]. With e.g., the HOG introduction [17] or SIFT of [18] the, conceptand may ofsuffer deep from learning, appearance traditional variations AEsof scenes were and extended as SAE byobjects adding [2]. With multiple the introduction layers, usually of the concept larger of than deep 2 learning, layers, totraditional form the AE deeps were structure extended ofas an AE. The detailedSAE by content adding ofmultiple SAE canlayers, be usually found large in [19r than,20]. 2 layers, to form the deep structure of an AE. The detailed content of SAE can be found in [19,20]. n Let a set of unlabeled training data be x1, x2, ::: , xn , where xi R n. Then, the structure of an AE Let a set of unlabeled training data bef {x , x ,..., x g }, where x 2 . Then, the structure of an can be depicted, as shown in Figure1. 12 n i AE can be depicted, as shown in Figure 1. FigureFigure 1. The1. The structure structure ofof an autoencoder autoencoder (AE (AE).). Algorithms 2019, 12, 122 3 of 11 The purpose of an AE is to learn representation by minimizing the construction loss (x, xˆ). L The weights between the input layer and hidden layer are called the encoder, and the weights between the hidden layer and output layer are called the decoder. The bottleneck code, also called code/latent representation, indicates the compressed knowledge representation of the original input. n 1 k 1 Hence, if we set the input vector x R × , the output vector of the encoder is h R × , and the n 1 2 2 output vector of the decoder is xˆ R × ; the representation code can be represented as 2 h = σh(ah) = σh(Wx + bh) (1) k 1 where σh( ) denotes the activation function of the hidden layer, ah R × is the summation of the · k n 2 k 1 hidden layer, W R × is the weight matrix between the input layer and hidden layer, and bh R × 2 2 denotes the bias vector of the hidden layer. The output of an AE is calculated as xˆ = σo(ao) =σo(Whˆ + bo) (2) n 1 where σo( ) denotes the activation function of the output layer, ao R × is the summation of the · n k 2 n 1 output layer, Wˆ R × is the weight matrix from the hidden layer to output layer, and bo R × is the 2 2 bias vector of the output layer.
Load more

Recommended publications
  • Enhancement of DBSCAN Algorithm and Transparency Clustering Of
    IJRECE VOL. 5 ISSUE 4 OCT.-DEC. 2017 ISSN: 2393-9028 (PRINT) | ISSN: 2348-2281 (ONLINE) Enhancement of DBSCAN Algorithm and Transparency Clustering of Large Datasets Kumari Silky1, Nitin Sharma2 1Research Scholar, 2Assistant Professor Institute of Engineering Technology, Alwar, Rajasthan, India Abstract: The data mining is the technique which can extract process of dividing the data into similar objects groups. A level useful information from the raw data. The clustering is the of simplification is achieved in case of less number of clusters technique of data mining which can group similar and dissimilar involved. But because of less number of clusters some of the type of information. The density based clustering is the type of fine details have been lost. With the use or help of clusters the clustering which can cluster data according to the density. The data is modeled. According to the machine learning view, the DBSCAN is the algorithm of density based clustering in which clusters search in a unsupervised manner and it is also as the EPS value is calculated which define radius of the cluster. The hidden patterns. The system that comes as an outcome defines a Euclidian distance will be calculated using neural networks data concept [3]. The clustering mechanism does not have only which calculate similarity in the more effective manner. The one step it can be analyzed from the definition of clustering. proposed algorithm is implemented in MATLAB and results are Apart from partitional and hierarchical clustering algorithms analyzed in terms of accuracy, execution time. number of new techniques has been evolved for the purpose of clustering of data.
    [Show full text]
  • Comparison of Dimensionality Reduction Techniques on Audio Signals
    Comparison of Dimensionality Reduction Techniques on Audio Signals Tamás Pál, Dániel T. Várkonyi Eötvös Loránd University, Faculty of Informatics, Department of Data Science and Engineering, Telekom Innovation Laboratories, Budapest, Hungary {evwolcheim, varkonyid}@inf.elte.hu WWW home page: http://t-labs.elte.hu Abstract: Analysis of audio signals is widely used and this work: car horn, dog bark, engine idling, gun shot, and very effective technique in several domains like health- street music [5]. care, transportation, and agriculture. In a general process Related work is presented in Section 2, basic mathe- the output of the feature extraction method results in huge matical notation used is described in Section 3, while the number of relevant features which may be difficult to pro- different methods of the pipeline are briefly presented in cess. The number of features heavily correlates with the Section 4. Section 5 contains data about the evaluation complexity of the following machine learning method. Di- methodology, Section 6 presents the results and conclu- mensionality reduction methods have been used success- sions are formulated in Section 7. fully in recent times in machine learning to reduce com- The goal of this paper is to find a combination of feature plexity and memory usage and improve speed of following extraction and dimensionality reduction methods which ML algorithms. This paper attempts to compare the state can be most efficiently applied to audio data visualization of the art dimensionality reduction techniques as a build- in 2D and preserve inter-class relations the most. ing block of the general process and analyze the usability of these methods in visualizing large audio datasets.
    [Show full text]
  • DBSCAN++: Towards Fast and Scalable Density Clustering
    DBSCAN++: Towards fast and scalable density clustering Jennifer Jang 1 Heinrich Jiang 2 Abstract 2, it quickly starts to exhibit quadratic behavior in high di- mensions and/or when n becomes large. In fact, we show in DBSCAN is a classical density-based clustering Figure1 that even with a simple mixture of 3-dimensional procedure with tremendous practical relevance. Gaussians, DBSCAN already starts to show quadratic be- However, DBSCAN implicitly needs to compute havior. the empirical density for each sample point, lead- ing to a quadratic worst-case time complexity, The quadratic runtime for these density-based procedures which is too slow on large datasets. We propose can be seen from the fact that they implicitly must compute DBSCAN++, a simple modification of DBSCAN density estimates for each data point, which is linear time which only requires computing the densities for a in the worst case for each query. In the case of DBSCAN, chosen subset of points. We show empirically that, such queries are proximity-based. There has been much compared to traditional DBSCAN, DBSCAN++ work done in using space-partitioning data structures such can provide not only competitive performance but as KD-Trees (Bentley, 1975) and Cover Trees (Beygelzimer also added robustness in the bandwidth hyperpa- et al., 2006) to improve query times, but these structures are rameter while taking a fraction of the runtime. all still linear in the worst-case. Another line of work that We also present statistical consistency guarantees has had practical success is in approximate nearest neigh- showing the trade-off between computational cost bor methods (e.g.
    [Show full text]
  • Density-Based Clustering of Static and Dynamic Functional MRI Connectivity
    Rangaprakash et al. Brain Inf. (2020) 7:19 https://doi.org/10.1186/s40708-020-00120-2 Brain Informatics RESEARCH Open Access Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment D. Rangaprakash1,2,3, Toluwanimi Odemuyiwa4, D. Narayana Dutt5, Gopikrishna Deshpande6,7,8,9,10,11,12,13* and Alzheimer’s Disease Neuroimaging Initiative Abstract Various machine-learning classifcation techniques have been employed previously to classify brain states in healthy and disease populations using functional magnetic resonance imaging (fMRI). These methods generally use super- vised classifers that are sensitive to outliers and require labeling of training data to generate a predictive model. Density-based clustering, which overcomes these issues, is a popular unsupervised learning approach whose util- ity for high-dimensional neuroimaging data has not been previously evaluated. Its advantages include insensitivity to outliers and ability to work with unlabeled data. Unlike the popular k-means clustering, the number of clusters need not be specifed. In this study, we compare the performance of two popular density-based clustering methods, DBSCAN and OPTICS, in accurately identifying individuals with three stages of cognitive impairment, including Alzhei- mer’s disease. We used static and dynamic functional connectivity features for clustering, which captures the strength and temporal variation of brain connectivity respectively. To assess the robustness of clustering to noise/outliers, we propose a novel method called recursive-clustering using additive-noise (R-CLAN). Results demonstrated that both clustering algorithms were efective, although OPTICS with dynamic connectivity features outperformed in terms of cluster purity (95.46%) and robustness to noise/outliers.
    [Show full text]
  • An Improvement for DBSCAN Algorithm for Best Results in Varied Densities
    Computer Engineering Department Faculty of Engineering Deanery of Higher Studies The Islamic University-Gaza Palestine An Improvement for DBSCAN Algorithm for Best Results in Varied Densities Mohammad N. T. Elbatta Supervisor Dr. Wesam M. Ashour A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Computer Engineering Gaza, Palestine (September, 2012) 1433 H ii stnkmegnelnonkcA Apart from the efforts of myself, the success of any project depends largely on the encouragement and guidelines of many others. I take this opportunity to express my gratitude to the people who have been instrumental in the successful completion of this project. I would like to thank my parents for providing me with the opportunity to be where I am. Without them, none of this would be even possible to do. You have always been around supporting and encouraging me and I appreciate that. I would also like to thank my brothers and sisters for their encouragement, input and constructive criticism which are really priceless. Also, special thanks goes to Dr. Homam Feqawi who did not spare any effort to review and audit my thesis linguistically. My heartiest gratitude to my wonderful wife, Nehal, for her patience and forbearance through my studying and preparing this study. I would like to express my sincere gratitude to my advisor Dr. Wesam Ashour for the continuous support of my master study and research, for his patience, motivation, enthusiasm, and immense knowledge. His guidance helped me in all the time of research and writing of this thesis. I could not have imagined having a better advisor and mentor for my master study.
    [Show full text]
  • Unsupervised Novelty Detection Using Deep Autoencoders with Density Based Clustering
    applied sciences Article Unsupervised Novelty Detection Using Deep Autoencoders with Density Based Clustering Tsatsral Amarbayasgalan 1, Bilguun Jargalsaikhan 1 and Keun Ho Ryu 1,2,* 1 Database and Bioinformatics Laboratory, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea; [email protected] (T.A.); [email protected] (B.J.) 2 Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam * Correspondence: [email protected] or [email protected]; Tel.: +82-43-261-2254 Received: 30 July 2018; Accepted: 22 August 2018; Published: 27 August 2018 Abstract: Novelty detection is a classification problem to identify abnormal patterns; therefore, it is an important task for applications such as fraud detection, fault diagnosis and disease detection. However, when there is no label that indicates normal and abnormal data, it will need expensive domain and professional knowledge, so an unsupervised novelty detection approach will be used. On the other hand, nowadays, using novelty detection on high dimensional data is a big challenge and previous research suggests approaches based on principal component analysis (PCA) and an autoencoder in order to reduce dimensionality. In this paper, we propose deep autoencoders with density based clustering (DAE-DBC); this approach calculates compressed data and error threshold from deep autoencoder model, sending the results to a density based cluster. Points that are not involved in any groups are not considered a novelty; the grouping points will be defined as a novelty group depending on the ratio of the points exceeding the error threshold. We have conducted the experiment by substituting components to show that the components of the proposed method together are more effective.
    [Show full text]
  • Comparative Study of Different Clustering Algorithms for Association Rule Mining Ms
    Ms. Pooja Gupta et al./ International Journal of Computer Science & Engineering Technology (IJCSET) Comparative Study of Different Clustering Algorithms for Association Rule Mining Ms. Pooja Gupta, Ms. Monika Jena, Computer science and Engineering Department, Amity University, Noida, India [email protected] [email protected] Ms. Manisha Chowdhary, Ms. Shilpi Singh, CSE Department, United group of Institutions, Greater Noida, India [email protected] [email protected] Abstract— In data mining, association rule mining is an important research area in today’s scenario. Various association rule mining can find interesting associations and correlation relationship among a large set of data items[1]. To find association rules for single dimensional database Apriori algorithm is appropriate. For large databases lots of candidate sets are generated. Thus Apriori algorithm is not efficient for large databases. We need some extension in the existing Apriori algorithm so that it can also work for large multidimensional database or quantitative database. For this purpose to work with apriori in large multidimensional database, data is divided into multiple data sets called as clusters. In order to divide large data bases into clusters we need various clustering algorithms which can be based on Statistical methods, Hierarchical methods, Density Based method or Grid based method. Once clusters are created by these clustering algorithms, the apriori algorithm can be easily applied on clusters of our interest for mining association rules. Since overall process of finding association rules highly depends on clustering algorithms so we have to use best suited clustering algorithm according to given data base ,thus overall execution time will be reduced.
    [Show full text]
  • Clustering Tips and Tricks in 45 Minutes (Maybe More :)
    Clustering Tips and Tricks in 45 minutes (maybe more :) Olfa Nasraoui, University of Louisville Tutorial for the Data Science for Social Good Fellowship 2015 cohort @DSSG2015@University of Chicago https://www.researchgate.net/profile/Olfa_Nasraoui Acknowledgment: many sources collected throughout years of teaching from textbook slides, etc, in particular Tan et al Roadmap • Problem Definition and Examples (2 min) • Major Families of Algorithms (2 min) • Recipe Book (15 min) 1. Types of inputs, Nature of data, Desired outputs ➔ Algorithm choice (5 min) 2. Nature of data ➔ Distance / Similarity measure choice (5 min) 3. Validation (5 min) 4. Presentation 5. Interpretation (5 min) • Challenges (10 min) o Expanding on 1 ▪ number of clusters, outliers, large data, high dimensional data, sparse data, mixed /heterogeneous data types, nestedness, domain knowledge: a few labels/constraints ▪ Python Info (10 min) Definition of Clustering • Finding groups of objects such that: – the objects in a group will be similar (or related) to one another and – the objects in a group will be different from (or unrelated to) the objects in other groups Inter-cluster distances are Intra-cluster maximized distances are minimized Application Examples • Marketing: Discover groups of customers with similar purchase patterns or similar demographics • Land use: Find areas of similar land use in an earth observation database • City-planning: Identify groups of houses according to their house type, value, and geographical location • Web Usage Profiling: find groups
    [Show full text]
  • A Novel Load Forecasting Approach Based on Smart Meter Data Using Advance Preprocessing and Hybrid Deep Learning
    applied sciences Article A Novel Load Forecasting Approach Based on Smart Meter Data Using Advance Preprocessing and Hybrid Deep Learning Fatih Ünal 1,* , Abdulaziz Almalaq 2 and Sami Ekici 1 1 Department of Energy Systems Engineering, Faculty of Technology, Firat University, Elazı˘g23110, Turkey; sekici@firat.edu.tr 2 Department of Electrical Engineering, College of Engineering, University of Hail, Hail 55476, Saudi Arabia; [email protected] * Correspondence: funal@firat.edu.tr Abstract: Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the customer-level, thanks to smart meters, is a complicated problem due to the high variability and uncertainty of load consumptions as well as customer privacy issues. In terms of customers’ short-term load forecasting, these models include a high level of nonlinearity between input data and output predictions, demanding more robustness, higher prediction accuracy, and generalizability. In this paper, we develop an advanced preprocessing technique coupled with a hybrid sequential learning-based energy forecasting model that employs a convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) within a unified framework for accurate energy consumption prediction. The energy consumption outliers and feature clustering are Citation: Ünal, F.; Almalaq, A.; extracted at the advanced preprocessing stage. The novel hybrid deep learning approach based on Ekici, S. A Novel Load Forecasting data features coding and decoding is implemented in the prediction stage.
    [Show full text]
  • Implementation of Data Mining Analysis to Determine the Tuna Fishing Zone Using DBSCAN Algorithm
    International Journal of Machine Learning and Computing, Vol. 9, No. 5, October 2019 Implementation of Data Mining Analysis to Determine the Tuna Fishing Zone Using DBSCAN Algorithm Muhammad Ramadhani and Devi Fitrianah catches will be processed to determine spatial data on tuna Abstract—The aim of this study is to map the tuna fishing fishing areas using DBSCAN algorithm and using rapidminer zones based on the daily fish catch data from the Hindian Ocean. as a tool for program execution. The contribution of this With the study, it is expected to deliver a potential tuna fishing study will be used for further study in many ways, one is to zones mapping, where it is based on the number of catch along deliver a potential tuna fishing zones mapping. with its spatial data. The study utilized a data mining approach with DBSCAN algorithm as the method to cluster the data. The study yields information that the Bigeye tuna is dominated the catch in the west monsoon, while Yellowfin tuna dominated the II. LITERATURE REVIEW catch in the east monsoon. Based on the trial using the DBSCAN algorithm, we know that the optimal Eps and MinPts value are A. Data Mining 1.5 and 5 respectively to generate a convergence cluster. Data mining is a branch of science that combines databases, statistics, artificial intelligence and machine learning [5]. Index Terms—Data mining, DBSCAN algorithm, spatial analysis, clustering, rapidminer. Examples of cases in data mining such as, search for names that are most commonly used in the US state or grouping documents from search results with search engines based on I.
    [Show full text]
  • Deep Trajectory Clustering with Autoencoders Xavier Olive, Luis Basora, Benoit Viry, Richard Alligier
    Deep Trajectory Clustering with Autoencoders Xavier Olive, Luis Basora, Benoit Viry, Richard Alligier To cite this version: Xavier Olive, Luis Basora, Benoit Viry, Richard Alligier. Deep Trajectory Clustering with Autoen- coders. ICRAT 2020, 9th International Conference for Research in Air Transportation, Sep 2020, Tampa, United States. hal-02916241 HAL Id: hal-02916241 https://hal-enac.archives-ouvertes.fr/hal-02916241 Submitted on 17 Aug 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. ICRAT 2020 Deep Trajectory Clustering with Autoencoders Xavier Olive∗, Luis Basora∗, Benoˆıt Viry∗, Richard Alligier† ∗ONERA – DTIS †Ecole´ Nationale de l’Aviation Civile Universite´ de Toulouse Universite´ de Toulouse Toulouse, France Toulouse, France Abstract—Identification and characterisation of air traffic fact that techniques such as Principal Component Analysis flows is an important research topic with many applications (PCA) can only generate linear embeddings, this two-step areas including decision-making support tools, airspace design or approach may be sub-optimal as the assumptions underlying traffic flow management. Trajectory clustering is an unsupervised data-driven approach used to automatically identify air traffic the dimensionality reduction and the clustering techniques flows in trajectory data.
    [Show full text]
  • Application of Deep Learning on Millimeter-Wave Radar Signals: a Review
    sensors Review Application of Deep Learning on Millimeter-Wave Radar Signals: A Review Fahad Jibrin Abdu , Yixiong Zhang * , Maozhong Fu, Yuhan Li and Zhenmiao Deng Department of Information and Communication Engineering, School of Informatics, Xiamen University, Xiamen 361005, China; [email protected] (F.J.A.); [email protected] (M.F.); [email protected] (Y.L.); [email protected] (Z.D.) * Correspondence: [email protected] Abstract: The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition, etc., to apply it to their respective problems. Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used. This is despite the vital potential of radars in adverse weather conditions, as well as their ability to simultaneously measure an object’s range and radial velocity seamlessly. As radar signals have not been exploited very much so far, there is a lack of available benchmark data. However, recently, there has been a lot of interest in applying radar data as input to various deep learning algorithms, as more datasets are being provided. To this end, this paper presents a survey of various deep learning approaches processing radar signals to accomplish some significant tasks in an autonomous driving application, such as detection and classification. We have itemized the review based on different radar signal representations, as it is one of the critical aspects while using radar data with deep learning models. Furthermore, we give an extensive review of the recent deep learning-based multi-sensor fusion models exploiting radar signals and camera images for object detection tasks.
    [Show full text]