Please wait a minute...
Vis Inf
Vis Inf  2018, Vol. 2 Issue (4): 225-234    DOI: 10.1016/j.visinf.2018.12.003
论文     
Versus——使用2AFC方法评估可视化和图像质量的工具
Jenny Vuonga,b, Sandeep Kaurc,d,  Julian Heinricha,d, Bosco K.Hoa, Christopher J.Hammange, Benedetta F.Baldib,a, Seán I.O’Donoghuea,b,c
aCSIRO, Data61, Australia
bGarvan Institute of Medical Research, Australia
cUniversity of New South Wales, Australia
dUniversity of Tübingen, Germany
eUniversity of Sydney, Australia
Versus—A tool for evaluating visualizations and image quality using a 2AFC methodology
Jenny Vuonga,b, Sandeep Kaurc,d,  Julian Heinricha,d, Bosco K.Hoa, Christopher J.Hammange, Benedetta F.Baldib,a, Seán I.O’Donoghuea,b,c
aCSIRO, Data61, Australia
bGarvan Institute of Medical Research, Australia
cUniversity of New South Wales, Australia
dUniversity of Tübingen, Germany
eUniversity of Sydney, Australia
 全文: PDF 
摘要： 面对科学领域出现的大量数据集，有必要开发新的可视化方法和策略，以便有所发现并找出至今尚未解决问题的答案。这些方法和策略不仅应能以简洁的图像来揭示科学发现，而且必须是有效和富有表现力的，不过这两个指标通常并未测试。本文提出Versus工具，它利用一种二选一的强制性选择方法（2AFC）和基于二叉树搜索的高效排序算法，可实现简单的图像质量评估和图像排位。该工具提供了一种通过网络构建评估实验的系统化方法，无需安装任何其他软件或编程技能。此外，Versus很容易接口众包平台（如亚马逊的Mechanical Turk）或作为独立系统来进行专家评估。本文展示了一项使用Versus进行图像评估的研究，该项研究旨在确定色彩、饱和度、亮度和纹理是否可取为判断三维蛋白质结构不确定性的良好指标。我们认为这种工具可集聚众包的力量，是有用的并且很有可能成为评测科学可视化结果有效性和表现力的简单快速的图像评估标准。
关键词： 评估;  可视化;  可视分析;  图像比较;  众包;  评价方法;  2AFC;  图像评估;  工具;  可视化评估    
Abstract: Novel visualization methods and strategies are necessary to cope with the deluge of datasets present in any scientific field to make discoveries and find answers to previously unanswered questions. These methods and strategies should not only present scientific findings as images in a concise way but also need to be effective and expressive, which often remain untested. Here, we present Versus, a tool to enable easy image quality assessment and image ranking, utilizing a two-alternative forced choice methodology (2AFC) and an efficient ranking algorithm based on a binary search. The tool provides a systematic way of setting up evaluation experiments via the web without the necessity to install any additional software or require any programming skills. Furthermore, Versus can easily interface with crowdsourcing platforms, such as Amazon’s Mechanical Turk, or can be used as a stand-alone system to carry out evaluations with experts. We demonstrate the use of Versus by means of an image evaluation study, aiming to determine if hue, saturation, brightness, and texture are good indicators of uncertainty in three-dimensional protein structures. Drawing from the power of crowdsourcing, we argue that there is demand and also great potential for this tool to become a standard for simple and fast image evaluations, with the aim to test the effectiveness and expressiveness of scientific visualizations.
Key words: Evaluation    Visualization    Visual analytics    Image comparison    Crowdsourcing    Evaluation methods    2AFC    Image evaluation    Tool    Visualization evaluation
出版日期: 2019-01-10
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
Jenny Vuong
Sandeep Kaur
Julian Heinrich
Bosco K.Ho
Christopher J.Hammang
Benedetta F.Baldi
Seán I.O’Donoghue

引用本文:

Jenny Vuong, Sandeep Kaur, Julian Heinrich, Bosco K.Ho, Christopher J.Hammang, Benedetta F.Baldi, Seán I.O’Donoghue. Versus—A tool for evaluating visualizations and image quality using a 2AFC methodology . Vis Inf, 2018, 2(4): 225-234.

链接本文:

http://www.zjujournals.com/vi/CN/10.1016/j.visinf.2018.12.003    或    http://www.zjujournals.com/vi/CN/Y2018/V2/I4/225

[1] Takanori Fujiwara, Tarik Crnovrsanin, Kwan-Liu Ma. 交互式网络分析过程的简明概括  [J]. Vis Inf, 2018, 2(4): 202-212.
[2] Aindrila Ghosh, Mona Nashaat, James Miller, Shaikh Quader, Chad Marston. 面向表格式工业数据集的探索性分析工具综述[J]. Vis Inf, 2018, 2(4): 235-253.
[3] Roger Almeida Leite, Theresia Gschwandtner, Silvia Miksch, Erich Gstrein, Johannes Kuntner. 采用可视化分析来检测欺诈事件 [J]. Vis Inf, 2018, 2(4): 191-201.
[4] Vahan Yoghourdjian, Daniel Archambault, Stephan Diehl, Tim Dwyer, Karsten Klein, Helen C.Purchase, Hsiang-Yun Wu. 探索复杂性的极限:图可视化实例研究综述[J]. Vis Inf, 2018, 2(4): 264-282.
[5] Shixia Liu, Gennady Andrienko, Yingcai Wu, Nan Cao, Liu Jiang, Conglei Shi, Yu-Shuen Wang, Seokhee Hong. 运用可视分析控制数据质量：数据复杂性挑战 [J]. Vis Inf, 2018, 2(4): 213-223.
[6] Indratmo, LeeHoworko, Joyce MariaBoedianto, BenDaniel. 采用堆叠条形图进行单个属性和整体属性比较的有效性 [J]. Vis Inf, 2018, 2(3): 155-165.
[7] Rulei Yu, Lei Shi. 深度学习可视化综述：面向用户群体分类 [J]. Vis Inf, 2018, 2(3): 147-154.
[8] Christopher Collins, Natalia Andrienko, TobiasSchreck, JingYang, Jaegul Choo, Ulrich Engelke, Amit Jena, Tim Dwyer. 人机分析过程导引 [J]. Vis Inf, 2018, 2(3): 166-180.
[9] Maha El Meseery, Orland Hoeber. 地理协同平行坐标（GCPC）：环境数据分析的现场试验研究 [J]. Vis Inf, 2018, 2(2): 111-124.
[10] DeqingLi, HonghuiMei, YiShen, ShuangSu, WenliZhang, JuntingWang, MingZu, WeiChen. ECharts: 是一款开源的、基于 web 的、跨平台的支持快速创建交互式可视化的框架[J]. Vis Inf, 2018, 2(2): 136-146.
[11] WenZhong, WeiXu, KevinG.Yager, GregoryS.Doerk, JianZhao, YunkeTiae, SungsooHa, CongXie, YuanZhong, KlausMueller, Kerstin KleeseVan Dam. MultiSciView: 从交叉数据空间视角进行X射线图像的多变量可视分析[J]. Vis Inf, 2018, 2(1): 14-25.
[12] KozenUmezawa, HiroakiNatsukawa, YosukeOnoue, KojiKoyamada. 可基于钙波数据进行假设推定的可视分析系统[J]. Vis Inf, 2018, 2(1): 2-13.
[13] Yusuke Ishikawa, Issei fujishiro. TideGrapher：橄榄球比赛战术的可视分析[J]. Vis Inf, 2018, 2(1): 60-70.
[14] Christina Gillmann, Robin G.C. Maack, Tobias Post, Thomas Wischgoll, Hans Hagen. 在知晓其不确定性的情况下运用分层图像语义进行锁孔手术规划的工作流程[J]. Vis Inf, 2018, 2(1): 26-36.
[15] Honghui Mei, Wei Chen, Yuxin Ma, HuihuaGua, Wanqi Hu. VisComposer:面向信息可视化的可编程集成开发环境 [J]. Vis Inf, 2018, 2(1): 71-81.