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Multi-target Image Detection Using Pigeon-Inspired Optimization based Species Mechanism

Multi-target Image Detection Using Pigeon-Inspired Optimizat...

Information Technology,Big data and Artificial Intelligence (ICIBA), International conference on IEEE International conference on

作者： Long Wang Jing Fan Xiao Wei Xincheng Cui Dongjie Wu National Computer System Engineering Research Institute of China Beijing China State-owned Changhong Machinery Factory Guilin China

ISBN: (数字)9781728152240

ISBN: (纸本)9781728152257

Multi-target detection (MTD) has a wide range of applications in target recognition, visual tracking, and other fields. The task of MTD is to find the sub-image set with the highest matching degree using the template image set from the source image. Therefore, this problem can be mapped to a multi-modal optimization problem. This paper combines the modified pigeon-inspired optimization (PIO) and species-based mechanism to perform multi-template matching tasks. To enhance the performance of our proposed method, the PIO is modified as follows: (1) the fitness of the seed is chosen as the switch signal of the map and compass operator and landmark operator; (2) in landmark operator, the halve mechanism is removed and a perturbation factor is introduced to enhance the exploration and exploitation ability. The species-based mechanism is utilized to search for multiple extreme points in parallel with multiple subpopulations. In addition, an expel mechanism is proposed to avoid the unnecessary fitness evaluation around the detected optima. Simulation results demonstrate that our proposed method performs better in detection accuracy and efficiency.

关键词： Optimization Statistics Sociology Compass Particle swarm optimization Switches Task analysis

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CCVis: visual analytics of student online learning behaviors using course clickstream data

CCVis: Visual analytics of student online learning behaviors...

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2019 conference on visualization and data analysis, VDA 2019

作者： Goulden, Maggie Celeste Gronda, Eric Yang, Yurou Zhang, Zihang Tao, Jun Wang, Chaoli Duan, Xiaojing Ambrose, G. Alex Abbott, Kevin Miller, Patrick Trinity College Dublin Ireland University of Maryland Baltimore County United States Zhejiang University China University of Notre Dame United States

As more and more college classrooms utilize online plat- forms to facilitate teaching and learning activities, analyzing stu- dent online behaviors becomes increasingly important for instruc- tors to effectively monitor and manage student progress and per- formance. In this paper, we present CCVis, a visual analytics tool for analyzing the course clickstream data and exploring student online learning behaviors. Targeting a large college introductory course with over two thousand student enrollments, our goal is to investigate student behavior patterns and discover the possi- ble relationships between student clickstream behaviors and their course performance. We employ higher-order network and struc- tural identity classification to enable visual analytics of behavior patterns from the massive clickstream data. CCVis includes four coordinated views (the behavior pattern, behavior breakdown, clickstream comparative, and grade distribution views) for user interaction and exploration. We demonstrate the effectiveness of CCVis through case studies along with an ad-hoc expert evalua- tion. Finally, we discuss the limitation and extension of this work. © 2019, Society for Imaging Science and Technology.

关键词： Students

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A visual Analytic approach to analyze Highway Vehicular Traffic 22

A Visual Analytic approach to analyze Highway Vehicular Traf...

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22nd International conference Information visualisation (iv) - Biomedical visualization, visualisation on Built and Rural Environments and Geometric Modelling and Imaging

作者： Buono, Paolo Legretto, Alessandra Ferilli, Stefano Angelastro, Sergio Univ Bari Dept Comp Sci Bari Italy

ISBN: (纸本)9781538672020

The Italian National Police started a research on vehicular traffic to improve road safety and reduce the number of theft victims. In order to support the discovery of anomalous behavior, this paper proposes a method for data analysis to automatically detect relevant hypotheses, a data mining technique to extract relevant information and a visualization technique. Traffic flow analysis is a challenging and complex task, due to the huge size of the data involved, thus falling in the realm of Big data. visual Analytics tools reduce and improve the search by representing a large amount of data in a small space through smart visualizations.

关键词： visual analytics outlier detections pattern mining traffic flow analysis big data

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The Human User in Progressive visual Analytics

The Human User in Progressive Visual Analytics

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21st Eurographics/IEEE VGT conference on visualization, EuroVis 2019

作者： Micallef, L. Schulz, H.-J. Angelini, M. Aupetit, M. Chang, R. Kohlhammer, J. Perer, A. Santucci, G. Aarhus University Denmark Sapienza University of Rome Italy Qatar Computing Research Institute HBKU Qatar Tufts University United States Fraunhofer IGD Darmstadt Germany Carnegie Mellon University United States

ISBN: (纸本)9783038680901

The amount of generated and analyzed data is ever increasing, and processing such large data sets can take too long in situations where time-to-decision or fluid data exploration are critical. Progressive visual analytics (PVA) has recently emerged as a potential solution that allows users to analyze intermediary results during the computation without waiting for the computation to complete. However, there has been limited consideration on how these techniques impact the user. Based on discussions from a Dagstuhl seminar held in October 2018, this paper characterizes PVA users by their common roles, their main tasks, and their distinct focus of analysis. It further discusses cognitive biases that play a particular role in PVA. This work will help PVA visualization designers in devising systems that are tailored for their specific target users and their characteristics. © 2019 The Author(s)

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SOM Flow: Guided Exploratory Cluster analysis with Self-Organizing Maps and Analytic Provenance

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 120-130页

作者： Sacha, Dominik Kraus, Matthias Bernard, Juergen Behrisch, Michael Schreck, Tobias Asano, Yuki Keim, Daniel A. Univ Konstanz Constance Germany Tech Univ Darmstadt Darmstadt Germany Graz Univ Technol Graz Austria Univ Tubingen Tubingen Germany

Clustering is a core building block for data analysis, aiming to extract otherwise hidden structures and relations from raw datasets, such as particular groups that can be effectively related, compared, and interpreted. A plethora of visual-interactive cluster analysis techniques has been proposed to date, however, arriving at useful clusterings often requires several rounds of user interactions to fine-tune the data preprocessing and algorithms. We present a multi-stage visual Analytics (VA) approach for iterative cluster refinement together with an implementation (SOMFlow) that uses Self-Organizing Maps (SOM) to analyze time series data. It supports exploration by offering the analyst a visual platform to analyze intermediate results, adapt the underlying computations, iteratively partition the data, and to reflect previous analytical activities. The history of previous decisions is explicitly visualized within a flow graph, allowing to compare earlier cluster refinements and to explore relations. We further leverage quality and interestingness measures to guide the analyst in the discovery of useful patterns, relations, and data partitions. We conducted two pair analytics experiments together with a subject matter expert in speech intonation research to demonstrate that the approach is effective for interactive data analysis, supporting enhanced understanding of clustering results as well as the interactive process itself.

关键词： visual Analytics Interaction visual Cluster analysis Quality Metrics Guidance Self -Organizing Maps Time Series

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Learning Relationship-Aware visual Features 15th

Learning Relationship-Aware Visual Features

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15th European conference on Computer Vision (ECCV)

作者： Messina, Nicola Amato, Giuseppe Carrara, Fabio Falchi, Fabrizio Gennaro, Claudio ISTI CNR Via G Moruzzi 1 I-56124 Pisa Italy

ISBN: (纸本)9783030110185;9783030110178

Relational reasoning in Computer Vision has recently shown impressive results on visual question answering tasks. On the challenging dataset called CLEVR, the recently proposed Relation Network (RN), a simple plug-and-play module and one of the state-of-the-art approaches, has obtained a very good accuracy (95.5%) answering relational questions. In this paper, we define a sub-field of Content-Based Image Retrieval (CBIR) called Relational-CBIR (R-CBIR), in which we are interested in retrieving images with given relationships among objects. To this aim, we employ the RN architecture in order to extract relation-aware features from CLEVR images. To prove the effectiveness of these features, we extended both CLEVR and Sort-of-CLEVR datasets generating a ground-truth for R-CBIR by exploiting relational data embedded into scene-graphs. Furthermore, we propose a modification of the RN module - a two-stage Relation Network (2S-RN) - that enabled us to extract relation-aware features by using a preprocessing stage able to focus on the image content, leaving the question apart. Experiments show that our RN features, especially the 2S-RN ones, outperform the RMAC state-of-the-art features on this new challenging task.

关键词： CLEVR Content-based image retrieval Deep learning Relational reasoning Relation networks Deep features

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River-water Quality exploration 13

River-water Quality Exploration

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13th IEEE conference on visual Analytics Science and Technology (VAST)

作者： Beham, Michael Splechtna, Rainer Gracanin, Denis Ginina, Elena Matkovic, Kresimir VRVis Res Ctr Vienna Austria Virginia Tech Blacksburg VA USA

ISBN: (纸本)9781538668610

We describe our analysis of VAST Challenge 2018 Mini-Challenge 2 data set using a collection of visualization tools. We used the tools for better user interaction and to introduce new views in support of visual analytics. We answer some of the challenge questions and plan further research on data exploration and analysis based on the newly introduced data model, interaction, and views.

关键词： Human-centered computing visualization visualization Techniques

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Prediction of Weather Radar Images via a Deep LSTM for Nowcasting

Prediction of Weather Radar Images via a Deep LSTM for Nowca...

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International Joint conference on Neural Networks (IJCNN)

作者： Guang Yao Zongxuan Liu Xufeng Guo Chaoshi Wei Xinfeng Li Zhihao Chen Em-Data AI Research Shanghai China China Eastern Region Air Traffic Management Bureau(ECATMB) Shanghai China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Weather radar images provide critical information for mesoscale weather nowcasting which plays significant roles in a range of fields including civil aviation and navigation. Differed from traditional radar exploration methods, this paper presents a novel prediction model based on a deep recurrent neural network (DeepRNN). The approach converts the task of nowcasting to a task of image series prediction. We first design a new loss function that pays more attention to the changes of images in the input sequence. In the mean while, an image discriminator is incorporated into the model to improve the visual quality of predicted images. Furthermore, optical flow is explored to preserve the the motion information. The prediction results are evaluated based on widely used statistic scores. The experimental results show that the proposed model leads to significant improvement in tasks of 2 hours forecasting of radar echo.

关键词： Predictive models Radar imaging Task analysis Optical flow Meteorology

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MTDES: Multi-dimensional Temporal data exploration System;Strong Support for Exploratory analysis Award in VAST 2018, Mini-Challenge 2 13

<i>MTDES</i>: Multi-dimensional Temporal Data Exploration Sy...

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13th IEEE conference on visual Analytics Science and Technology (VAST)

作者： Pham, Vung V. Dang, Tommy Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

ISBN: (纸本)9781538668610

This work proposes a visual analytic solution which is well-designed to provide investigative functions with fluent interactions to analyze multi-dimensional temporal data. The solution allows users to view different dimensions of the data at different levels of details with a well-designed mixture of different visualizations and smooth interactions. At the general/overview level, various aggregation strategies are used to reduce data to be visualized, and different sorting procedures are used to cluster correlated data together to help discover patterns. Detail views are provided to explore and confirm/reject the identified patterns. Interaction and smooth transition between views are implemented to enable natural actions while performing analysis tasks. This work also presents the result of applying the solution to the VAST 2018 - Mini-Challenge (MC) 2 dataset, which led to the Strong Support for Exploratory analysis award for the challenge.

关键词： Multi-dimensional, temporal data analysis-Heat-map-Line-graph-Interaction Design VAST Challenge 2018-Mini-Challenge 2-Award: Strong Support for Exploratory analysis

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A visual Analytics GUI for Multigranular Spatio-Temporal exploration and Comparison of Open Mobility data 22

A Visual Analytics GUI for Multigranular Spatio-Temporal Exp...

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22nd International conference Information visualisation (iv) - Biomedical visualization, visualisation on Built and Rural Environments and Geometric Modelling and Imaging

作者： Robino, Camilla Di Rocco, Laura Guerrini, Giovanna Di Martino, Sergio Bertolotto, Michela Univ Genoa Genoa Italy Univ Napoli Federico II Naples Italy Univ Coll Dublin Dublin Ireland

ISBN: (纸本)9781538672020

Recent technological developments in the fields of positioning and mobile communications gave rise to the availability of massive spalio-temporal open datasets about cities. A proper exploitation of these big datasets by decision makers of smart cities could be very useful to analyse and understand mobility patterns, with the final goal of easing many transportation problems, like parking search and traffic. While many research efforts have been aimed at defining powerful visual analytics tools for exploring vehicular trajectory data, to date almost no specifically tailored tools are available to analyse (on-street) parking data and dynamics. To fill this gap, in this paper we present the current state of an on-going research on the development of a visual analytics tool, meant to support decision makers of smart cities in performing multigranular spatio-temporal explorations of mobility open data, like those about parking. Moreover, the proposed GUI offers the possibility to overlay external spatio-temporal datasets as well as to customize the way this data is rendered, to get a better insight on the parking dynamics and its influencing factors.

关键词： data visualization Graphical user interfaces Tools Bars visual analytics Smart cities Roads

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