检索结果-内蒙古大学图书馆

40th Annual Conference on European Association for Computer Graphics, EUROGRAPHICS 2019

作者： McNabb, Liam Laramee, Robert S. Visual and Interactive Computing Group Swansea University United Kingdom

This paper attempts to explain the mechanics of writing a survey paper in data visualization or visual analytics. It serves as a useful starting point for those who have never written a survey paper or have very little experience. A literature review or survey paper is often considered the starting point of a PhD candidate's scientific degree. However, there are no dedicated papers that focus on guidelines for the planning or writing of a survey paper or literature review in visualization or visual analytics. We provide guidelines and our recommendations for a foundational structure on which to build a survey paper, whilst also considering intermediate goals, and offer helpful advice to improve the survey process and literature analysis. The result is a useful starting point for those wishing to write a survey paper or state-of-the-art (STAR) review in visualization or visual analytics. The guidelines and recommendations we make can also be generalized to other areas of computing and science. An abstract is a required feature of a survey paper and should identity the topic of the literature review. A good abstract addresses why the given topic is interesting and why it is helpful. A good abstract features the following elements: (1) topic introduction, (2) the motivation, (3) the goal of the review, and the benefits the review provides to the reader. A good literature survey offers a helpful classification of the literature, mature areas of research, and open, unsolved problems in visualization or visual analytics. © 2019 The Author(s) Eurographics Proceedings © 2019 The Eurographics Association.

关键词： Surveys

来源：评论

学校读者我要写书评

暂无评论

Beyond Entertainment: An Investigation of Externalization Design in Video Games

引用

Computer Graphics Forum 2025年

作者： Becker, F. Warnking, R.P. Brückler, H. Blascheck, T. University of Stuttgart Institute for Visualization and Interactive Systems Germany Heidelberg University Medical Faculty Mannheim Department of Biomedical Informatics Germany University of Osnabrück Institute for Computer Science Visual Computing Group Germany

This article investigates when and how video games enable players to create externalizations in a diverse sample of 388 video games. We follow a grounded-theory approach, extracting externalizations from video games to explore design ideas and relate them to practices in visualization. Video games often engage players in problem-solving activities, like solving a murder mystery or optimizing a strategy, requiring players to interpret heterogeneous data—much like tasks in the visualization domain. In many cases, externalizations can help reduce a user's mental load by making tangible what otherwise only lives in their head, acting as external storage or a visual playground. Over five coding phases, we created a hierarchy of 277 tags to describe the video games in our collection, from which we extracted 169 externalizations. We characterize these externalizations along nine dimensions like mental load, visual encodings, and motivations, resulting in 13 categories divided into four clusters: quick access, storage, sensemaking, and communication. We formulate considerations to guide future work, looking at tasks and challenges, naming potentials for inspiration, and discussing which topics could advance the state of externalization. © 2025 The Author(s). Computer Graphics Forum published by Eurographics - The European Association for Computer Graphics and John Wiley & Sons Ltd.

关键词： Flow visualization

来源：评论

学校读者我要写书评

暂无评论

Cartograms with topological features 36

Cartograms with topological features

引用

36th Annual Conference on Computer Graphics and visual computing, CGVC 2018

作者： Tong, Chao McNabb, Liam Laramee, Robert S. Visual and Interactive Computing Group Swansea University United Kingdom

ISBN: (纸本)9783038680710

Cartograms are a popular and useful technique for depicting geo-spatial data. Dorling style and rectangular cartograms are very good for facilitating comparisons between unit areas. Each unit area is represented by the same shape such as a circle or rectangle, and the uniformity in shapes facilitates comparative judgment. However, the layout of these more abstract shapes may also simultaneously reduce the map’s legibility and increase error. When we integrate univariate data into a cartogram, the recognizability of cartogram may be reduced. There is a trade-off between information recognition and geo-information accuracy. This is the inspiration behind the work we present. We attempt to increase the map’s recognizability and reduce error by introducing topological features into the cartographic map. Our goal is to include topological features such as a river in a Dorling-style or rectangular cartogram to make the visual layout more recognizable, increase map cognition and reduce geo-spatial error. We believe that compared to the standard Dorling and rectangular style cartogram, adding topological features provides familiar geo-spatial cues and flexibility to enhance the recognizability of a cartogram. © 2018 The Author(s) Eurographics Proceedings © 2018 The Eurographics Association.

关键词： Errors

来源：评论

学校读者我要写书评

暂无评论

When size matters: Towards evaluating perceivability of choropleths 36

When size matters: Towards evaluating perceivability of chor...

引用

36th Annual Conference on Computer Graphics and visual computing, CGVC 2018

作者： McNabb, Liam Laramee, Robert S. Wilson, Max Visual and Interactive Computing Group Swansea University United Kingdom School of Computer Science University of Nottingham United Kingdom

ISBN: (纸本)9783038680710

Choropleth maps are an invaluable visualization type for mapping geo-spatial data. One advantage to a choropleth map over other geospatial visualizations such as cartograms is the familiarity of a non-distorted landmass. However, this causes challenges when an area becomes too small in order to accurately perceive the underlying color. When does size matter in a choropleth map? We conduct an experiment to verify the relationship between choropleth maps, their underlying color map, and a user’s perceivability. We do this by testing a user’s perception of color relative to an administrative area’s size within a choropleth map, as well as user-preference of fixed-locale maps with enforced minimum areas. Based on this initial experiment we can make the first recommendations with respect to a unit area’s minimum size in order to be perceivably useful. © 2018 The Author(s) Eurographics Proceedings © 2018 The Eurographics Association.

关键词： Color

来源：评论

学校读者我要写书评

暂无评论

Cartographic treemaps for visualization of public healthcare data 35

Cartographic treemaps for visualization of public healthcare...

引用

35th Annual Conference on Computer Graphics and visual computing, CGVC 2017

作者： Tong, Chao Roberts, Richard Laramee, Robert S. Berridge, Damon Thayer, Daniel Visual and Interactive Computing Group Swansea University United Kingdom Medical School Swansea University United Kingdom

ISBN: (纸本)9783038680505

The National healthcare Service (NHS) in the UK collects a massive amount of high-dimensional, region-centric data concerning individual healthcare units throughout Great Britain. It is challenging to visually couple the large number of multivariate attributes about each region unit together with the geo-spatial location of the clinical practices for visual exploration, analysis, and comparison. We present a novel multivariate visualization we call a cartographic treemap that attempts to combine the space-filling advantages of treemaps for the display of hierarchical, multivariate data together with the relative geo-spatial location of NHS practices in the form of a modified cartogram. It offers both space filling and geospatial error metrics that provide the user with interactive control over the space-filling versus geographic error trade-off. The result is a visualization that offers users a more space efficient overview of the complex, multivariate healthcare data coupled with the relative geo-spatial location of each practice to enable and facilitate exploration, analysis, and comparison. We evaluate the two metrics and demonstrate the use of our approach on real, large high-dimensional NHS data and derive a number of multivariate observations based on healthcare in the UK as a result. We report the reaction of our software from two domain experts in health science. © 2017 The Author(s) Eurographics Proceedings © 2017 The Eurographics Association.

关键词： visualization

来源：评论

学校读者我要写书评

暂无评论

Time-oriented cartographic treemaps for visualization of public healthcare data 35

Time-oriented cartographic treemaps for visualization of pub...

引用

35th Annual Conference on Computer Graphics and visual computing, CGVC 2017

作者： Tong, Chao McNabb, Liam Laramee, Robert S. Lyons, Jane Walters, Angharad Berridge, Damon Thayer, Daniel Visual and Interactive Computing Group Swansea University United Kingdom Medical School Swansea University United Kingdom

ISBN: (纸本)9783038680505

Cartographic treemaps offer a way to explore and present hierarchical multi-variate data that combines the space-efficient advantages of treemaps for the display of hierarchical data together with relative geo-spatial location from maps in the form of a modified cartogram. They offer users a space-efficient overview of the complex, multi-variate data coupled with the relative geo-spatial location to enable and facilitate exploration, analysis, and comparison. In this paper, we introduce time as an additional variate, in order to develop time-oriented cartographic treemaps. We design, implement and compare a range of visual layout options highlighting advantages and disadvantage of each. We apply the method to the study of UK-centric electronic health records data as a case study. We use the results to explore the trends of a range of health diagnoses in each UK healthcare region over multiple years exploiting both static and animated visual designs. We provide several examples and user options to evaluate the performance in exploration, analysis, and comparison. We also report the reaction of domain experts from health science. © 2017 The Author(s) Eurographics Proceedings © 2017 The Eurographics Association.

关键词： Forestry

来源：评论

学校读者我要写书评

暂无评论

Coherence maps for blood flow exploration

Coherence maps for blood flow exploration

引用

2016 Eurographics Workshop on visual computing for Biology and Medicine, VCBM 2016

作者： Englund, Rickard Ropinski, Timo Hotz, Ingrid Interactive Visualization Group Linköping University Sweden Visual Computing Group Ulm University Germany

ISBN: (纸本)9783038680109

Blood flow data from direct measurements (4D flow MRI) or numerical simulations opens new possibilities for the understanding of the development of cardiac diseases. However, before this new data can be used in clinical studies or for diagnosis, it is important to develop a notion of the characteristics of typical flow structures. To support this process we developed a novel blood flow clustering and exploration method. The method builds on the concept of coherent flow structures. Coherence maps for cross-sectional slices are defined to show the overall degree of coherence of the flow. In coherent regions the method summarizes the dominant blood flow using a small number of pathline representatives. In contrast to other clustering approaches the clustering is restricted to coherent regions and pathlines with low coherence values, which are not suitable for clustering and thus are not forced into clusters. The coherence map is based on the Finite-time Lyapunov Exponent (FTLE). It is created on selected planes in the inflow respective outflow area of a region of interest. The FTLE value measures the rate of separation of pathlines originating from this plane. Different to previous work using FTLE we do not focus on separating extremal lines but on local minima and regions of low FTLE intensities to extract coherent flow. The coherence map and the extracted clusters serve as basis for the flow exploration. The extracted clusters can be selected and inspected individually. Their flow rate and coherence provide a measure for their significance. Switching off clusters reduces the amount of occlusion and reveals the remaining part of the flow. The non-coherent regions can also be explored by interactive manual pathline seeding in the coherence map. © 2016 The Author(s) Eurographics Proceedings © 2016 The Eurographics Association.

关键词： Blood

来源：评论

学校读者我要写书评

暂无评论

Why is the Winner the Best?

Why is the Winner the Best?

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Biomedical image analysis competitions: The state of current participation practice

arXiv

引用

arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

A crowdsourcing system for integrated and reproducible evaluation in scientific visualization

A crowdsourcing system for integrated and reproducible evalu...

引用

Pacific (formerly Asia-Pacific APVIS) visualization Symposium

作者： Rickard Englund Sathish Kottravel Timo Ropinski Linkopings universitet Linkoping SE Interactive Visualization Group Linkoping University Sweden Visual Computing Group Ulm University Germany

ISBN: (纸本)9781509014521

User evaluations have gained increasing importance in visualization research over the past years, as in many cases these evaluations are the only way to support the claims made by visualization researchers. Unfortunately, recent literature reviews show that in comparison to algorithmic performance evaluations, the number of user evaluations is still very low. Reasons for this are the required amount of time to conduct such studies together with the difficulties involved in participant recruitment and result reporting. While it could be shown that the quality of evaluation results and the simplified participant recruitment of crowdsourcing platforms makes this technology a viable alternative to lab experiments when evaluating visualizations, the time for conducting and reporting such evaluations is still very high. In this paper, we propose a software system, which integrates the conduction, the analysis and the reporting of crowdsourced user evaluations directly into the scientific visualization development process. With the proposed system, researchers can conduct and analyze quantitative evaluations on a large scale through an evaluation-centric user interface with only a few mouse clicks. Thus, it becomes possible to perform iterative evaluations during algorithm design, which potentially leads to better results, as compared to the time consuming user evaluations traditionally conducted at the end of the design process. Furthermore, the system is built around a centralized database, which supports an easy reuse of old evaluation designs and the reproduction of old evaluations with new or additional stimuli, which are both driving challenges in scientific visualization research. We will describe the system's design and the considerations made during the design process, and demonstrate the system by conducting three user evaluations, all of which have been published before in the visualization literature.

关键词： Data visualization Crowdsourcing visualization Algorithm design and analysis Recruitment Prediction algorithms Electronic mail

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：