Visual Informatics最新文献_第9页

A uncertainty visual analytics approach for bus travel time 公交出行时间的不确定性可视化分析方法

IF 3 3区计算机科学

Visual Informatics Pub Date : 2022-12-01 DOI: 10.1016/j.visinf.2022.06.002

Weixin Zhao , Guijuan Wang , Zhong Wang , Liang Liu , Xu Wei , Yadong Wu

{"title":"A uncertainty visual analytics approach for bus travel time","authors":"Weixin Zhao , Guijuan Wang , Zhong Wang , Liang Liu , Xu Wei , Yadong Wu","doi":"10.1016/j.visinf.2022.06.002","DOIUrl":"10.1016/j.visinf.2022.06.002","url":null,"abstract":"<div><p>Bus travel time is uncertain due to the dynamic change in the environment. Passenger analyzing bus travel time uncertainty has significant implications for understanding bus running errors and reducing travel risks. To quantify the uncertainty of the bus travel time prediction model, a visual analysis method about the bus travel time uncertainty is proposed in this paper, which can intuitively obtain uncertain information of bus travel time through visual graphs. Firstly, a Bayesian encoder–decoder deep neural network (BEDDNN) model is proposed to predict the bus travel time. The BEDDNN model outputs results with distributional properties to calculate the prediction model uncertainty degree and provide the estimation of the bus travel time uncertainty. Second, an interactive uncertainty visualization system is developed to analyze the time uncertainty associated with bus stations and lines. The prediction model and the visualization model are organically combined to better demonstrate the prediction results and uncertainties. Finally, the model evaluation results based on actual bus data illustrate the effectiveness of the model. The results of the case study and user evaluation show that the visualization system in this paper has a positive impact on the effectiveness of conveying uncertain information and on user perception and decision making.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"6 4","pages":"Pages 1-11"},"PeriodicalIF":3.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468502X22000638/pdfft?md5=ccdb87f99aecb534c2895ffeed825848&pid=1-s2.0-S2468502X22000638-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130811383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Interactive lighting editing system for single indoor low-light scene images with corresponding depth maps 交互式灯光编辑系统，用于单个室内低光场景图像与相应的深度图

IF 3 3区计算机科学

Visual Informatics Pub Date : 2022-12-01 DOI: 10.1016/j.visinf.2022.08.001

Zhongyun Bao, Gang Fu, Lian Duan, Chunxia Xiao

{"title":"Interactive lighting editing system for single indoor low-light scene images with corresponding depth maps","authors":"Zhongyun Bao, Gang Fu, Lian Duan, Chunxia Xiao","doi":"10.1016/j.visinf.2022.08.001","DOIUrl":"10.1016/j.visinf.2022.08.001","url":null,"abstract":"<div><p>We propose a novel interactive lighting editing system for lighting a single indoor RGB image based on spherical harmonic lighting. It allows users to intuitively edit illumination and relight the complicated low-light indoor scene. Our method not only achieves plausible global relighting but also enhances the local details of the complicated scene according to the spatially-varying spherical harmonic lighting, which only requires a single RGB image along with a corresponding depth map. To this end, we first present a joint optimization algorithm, which is based on the geometric optimization of the depth map and intrinsic image decomposition avoiding texture-copy, for refining the depth map and obtaining the shading map. Then we propose a lighting estimation method based on spherical harmonic lighting, which not only achieves the global illumination estimation of the scene, but also further enhances local details of the complicated scene. Finally, we use a simple and intuitive interactive method to edit the environment lighting map to adjust lighting and relight the scene. Through extensive experimental results, we demonstrate that our proposed approach is simple and intuitive for relighting the low-light indoor scene, and achieve state-of-the-art results.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"6 4","pages":"Pages 90-99"},"PeriodicalIF":3.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468502X22000882/pdfft?md5=9c9150254f62643a645f9ca15efd2ffd&pid=1-s2.0-S2468502X22000882-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130964210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Towards a better understanding of the role of visualization in online learning: A review 更好地理解可视化在在线学习中的作用:综述

IF 3 3区计算机科学

Visual Informatics Pub Date : 2022-12-01 DOI: 10.1016/j.visinf.2022.09.002

Gefei Zhang, Zihao Zhu, Sujia Zhu, Ronghua Liang, Guodao Sun

{"title":"Towards a better understanding of the role of visualization in online learning: A review","authors":"Gefei Zhang, Zihao Zhu, Sujia Zhu, Ronghua Liang, Guodao Sun","doi":"10.1016/j.visinf.2022.09.002","DOIUrl":"10.1016/j.visinf.2022.09.002","url":null,"abstract":"<div><p>With the popularity of online learning in recent decades, MOOCs (Massive Open Online Courses) are increasingly pervasive and widely used in many areas. Visualizing online learning is particularly important because it helps to analyze learner performance, evaluate the effectiveness of online learning platforms, and predict dropout risks. Due to the large-scale, high-dimensional, and heterogeneous characteristics of the data obtained from online learning, it is difficult to find hidden information. In this paper, we review and classify the existing literature for online learning to better understand the role of visualization in online learning. Our taxonomy is based on four categorizations of online learning tasks: behavior analysis, behavior prediction, learning pattern exploration and assisted learning. Based on our review of relevant literature over the past decade, we also identify several remaining research challenges and future research work.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"6 4","pages":"Pages 22-33"},"PeriodicalIF":3.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468502X22000924/pdfft?md5=6b07edcfd3ec7f98bc46d186255d7604&pid=1-s2.0-S2468502X22000924-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122494430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A survey of visual analytics techniques for online education 在线教育的可视化分析技术调查

IF 3 3区计算机科学

Visual Informatics Pub Date : 2022-12-01 DOI: 10.1016/j.visinf.2022.07.004

Xiaoyan Kui, Naiming Liu, Qiang Liu, Jingwei Liu, Xiaoqian Zeng, Chao Zhang

引用次数: 3

Visualization and visual analysis of multimedia data in manufacturing: A survey 制造业中多媒体数据的可视化与可视化分析:综述

IF 3 3区计算机科学

Visual Informatics Pub Date : 2022-12-01 DOI: 10.1016/j.visinf.2022.09.001

Yunchao Wang, Zihao Zhu, Lei Wang, Guodao Sun, Ronghua Liang

{"title":"Visualization and visual analysis of multimedia data in manufacturing: A survey","authors":"Yunchao Wang, Zihao Zhu, Lei Wang, Guodao Sun, Ronghua Liang","doi":"10.1016/j.visinf.2022.09.001","DOIUrl":"10.1016/j.visinf.2022.09.001","url":null,"abstract":"<div><p>With the development of production technology and social needs, sectors of manufacturing are constantly improving. The use of sensors and computers has made it increasingly convenient to collect multimedia data in manufacturing. Targeted, rapid, and detailed analysis based on the type of multimedia data can make timely decisions at different stages of the entire manufacturing process. Visualization and visual analytics are frequently adopted in multimedia data analysis of manufacturing because of their powerful ability to understand, present, and analyze data intuitively and interactively. In this paper, we present a literature review of visualization and visual analytics specifically for manufacturing multimedia data. We classify existing research according to visualization techniques, interaction analysis methods, and application areas. We discuss the differences when visualization and visual analytics are applied to different types of multimedia data in the context of particular examples of manufacturing research projects. Finally, we summarize the existing challenges and prospective research directions.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"6 4","pages":"Pages 12-21"},"PeriodicalIF":3.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468502X22000912/pdfft?md5=7a4420f6c48211e2a2b1aa7571c6e640&pid=1-s2.0-S2468502X22000912-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131576961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A review of feature fusion-based media popularity prediction methods 基于特征融合的媒体流行度预测方法综述

IF 3 3区计算机科学

Visual Informatics Pub Date : 2022-12-01 DOI: 10.1016/j.visinf.2022.07.003

An-An Liu , Xiaowen Wang , Ning Xu , Junbo Guo , Guoqing Jin , Quan Zhang , Yejun Tang , Shenyuan Zhang

{"title":"A review of feature fusion-based media popularity prediction methods","authors":"An-An Liu , Xiaowen Wang , Ning Xu , Junbo Guo , Guoqing Jin , Quan Zhang , Yejun Tang , Shenyuan Zhang","doi":"10.1016/j.visinf.2022.07.003","DOIUrl":"10.1016/j.visinf.2022.07.003","url":null,"abstract":"<div><p>With the popularization of social media, the way of information transmission has changed, and the prediction of information popularity based on social media platforms has attracted extensive attention. Feature fusion-based media popularity prediction methods focus on the multi-modal features of social media, which aim at exploring the key factors affecting media popularity. Meanwhile, the methods make up for the deficiency in feature utilization of traditional methods based on information propagation processes. In this paper, we review feature fusion-based media popularity prediction methods from the perspective of feature extraction and predictive model construction. Before that, we analyze the influencing factors of media popularity to provide intuitive understanding. We further argue about the advantages and disadvantages of existing methods and datasets to highlight the future directions. Finally, we discuss the applications of popularity prediction. To the best of our knowledge, this is the first survey reporting feature fusion-based media popularity prediction methods.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"6 4","pages":"Pages 78-89"},"PeriodicalIF":3.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468502X22000869/pdfft?md5=3f5928b7e56ee9c39a226fe68dbcb36d&pid=1-s2.0-S2468502X22000869-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123810950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A review of image and video colorization: From analogies to deep learning 回顾图像和视频着色:从类比到深度学习

IF 3 3区计算机科学

Visual Informatics Pub Date : 2022-09-01 DOI: 10.1016/j.visinf.2022.05.003

Shu-Yu Chen , Jia-Qi Zhang , You-You Zhao , Paul L. Rosin , Yu-Kun Lai , Lin Gao

{"title":"A review of image and video colorization: From analogies to deep learning","authors":"Shu-Yu Chen , Jia-Qi Zhang , You-You Zhao , Paul L. Rosin , Yu-Kun Lai , Lin Gao","doi":"10.1016/j.visinf.2022.05.003","DOIUrl":"10.1016/j.visinf.2022.05.003","url":null,"abstract":"<div><p>Image colorization is a classic and important topic in computer graphics, where the aim is to add color to a monochromatic input image to produce a colorful result. In this survey, we present the history of colorization research in chronological order and summarize popular algorithms in this field. Early work on colorization mostly focused on developing techniques to improve the colorization quality. In the last few years, researchers have considered more possibilities such as combining colorization with NLP (natural language processing) and focused more on industrial applications. To better control the color, various types of color control are designed, such as providing reference images or color-scribbles. We have created a taxonomy of the colorization methods according to the input type, divided into grayscale, sketch-based and hybrid. The pros and cons are discussed for each algorithm, and they are compared according to their main characteristics. Finally, we discuss how deep learning, and in particular Generative Adversarial Networks (GANs), has changed this field.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"6 3","pages":"Pages 51-68"},"PeriodicalIF":3.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468502X22000389/pdfft?md5=16a081f691f2d75368094f26919578af&pid=1-s2.0-S2468502X22000389-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114109871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Identifying the skeptics and the undecided through visual cluster analysis of local network geometry 通过局部网络几何图形的视觉聚类分析识别怀疑论者和未决定者

IF 3 3区计算机科学

Visual Informatics Pub Date : 2022-09-01 DOI: 10.1016/j.visinf.2022.07.002

Shenghui Cheng , Joachim Giesen , Tianyi Huang , Philipp Lucas , Klaus Mueller

引用次数: 7

MDIVis: Visual analytics of multiple destination images on tourism user generated content MDIVis:对旅游用户生成内容的多个目的地图像进行可视化分析

IF 3 3区计算机科学

Visual Informatics Pub Date : 2022-09-01 DOI: 10.1016/j.visinf.2022.06.001

Changlin Li , Mengqi Cao , Xiaolin Wen , Haotian Zhu , Shangsong Liu , Xinyi Zhang , Min Zhu

{"title":"MDIVis: Visual analytics of multiple destination images on tourism user generated content","authors":"Changlin Li , Mengqi Cao , Xiaolin Wen , Haotian Zhu , Shangsong Liu , Xinyi Zhang , Min Zhu","doi":"10.1016/j.visinf.2022.06.001","DOIUrl":"https://doi.org/10.1016/j.visinf.2022.06.001","url":null,"abstract":"<div><p>Abundant tourism user-generated content (UGC) contains a wealth of cognitive and emotional information, providing valuable data for building destination images that depict tourists’ experiences and appraisal of the destinations during the tours. In particular, multiple destination images can assist tourism managers in exploring the commonalities and differences to investigate the elements of interest of tourists and improve the competitiveness of the destinations. However, existing methods usually focus on the image of a single destination, and they are not adequate to analyze and visualize UGC to extract valuable information and knowledge. Therefore, we discuss requirements with tourism experts and present MDIVis, a multi-level interactive visual analytics system that allows analysts to comprehend and analyze the cognitive themes and emotional experiences of multiple destination images for comparison. Specifically, we design a novel sentiment matrix view to summarize multiple destination images and improve two classic views to analyze the time-series pattern and compare the detailed information of images. Finally, we demonstrate the utility of MDIVis through three case studies with domain experts on real-world data, and the usability and effectiveness are confirmed through expert interviews.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"6 3","pages":"Pages 1-10"},"PeriodicalIF":3.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468502X22000419/pdfft?md5=b795f3316fcfff3cd7b997f4dbfa5e4e&pid=1-s2.0-S2468502X22000419-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91620075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

VisuaLizations As Intermediate Representations (VLAIR): An approach for applying deep learning-based computer vision to non-image-based data 可视化作为中间表示(VLAIR):一种将基于深度学习的计算机视觉应用于非图像数据的方法

IF 3 3区计算机科学

Visual Informatics Pub Date : 2022-09-01 DOI: 10.1016/j.visinf.2022.05.001

Ai Jiang , Miguel A. Nacenta , Juan Ye

{"title":"VisuaLizations As Intermediate Representations (VLAIR): An approach for applying deep learning-based computer vision to non-image-based data","authors":"Ai Jiang , Miguel A. Nacenta , Juan Ye","doi":"10.1016/j.visinf.2022.05.001","DOIUrl":"10.1016/j.visinf.2022.05.001","url":null,"abstract":"<div><p>Deep learning algorithms increasingly support automated systems in areas such as human activity recognition and purchase recommendation. We identify a current trend in which data is transformed first into abstract visualizations and then processed by a computer vision deep learning pipeline. We call this VisuaLization As Intermediate Representation (VLAIR) and believe that it can be instrumental to support accurate recognition in a number of fields while also enhancing humans’ ability to interpret deep learning models for debugging purposes or for personal use. In this paper we describe the potential advantages of this approach and explore various visualization mappings and deep learning architectures. We evaluate several VLAIR alternatives for a specific problem (human activity recognition in an apartment) and show that VLAIR attains classification accuracy above classical machine learning algorithms and several other non-image-based deep learning algorithms with several data representations.</p></div>","PeriodicalId":36903,"journal":{"name":"Visual Informatics","volume":"6 3","pages":"Pages 35-50"},"PeriodicalIF":3.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468502X22000365/pdfft?md5=4cc6e01dd1fe8dfea6194fce4dffdeef&pid=1-s2.0-S2468502X22000365-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131868616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4