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Rene Cutura

Doctoral Researcher
Visualization Research Center (VISUS)
Sedlmair Group

Contact

Allmandring 19
70569 Stuttgart
Germany

Publications

2023
1. Morariu, Cristina ; Bibal, Adrien ; Cutura, Rene ; Frénay, Beno\^ıt ; Sedlmair, Michael: Predicting User Preferences of Dimensionality Reduction Embedding Quality. In: IEEE Transactions on Visualization and Computer Graphics, IEEE Transactions on Visualization and Computer Graphics. Bd. 29 (2023), Nr. 1, S. 745–755
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  @article{morariu2023dumbledr, abstract = {A plethora of dimensionality reduction techniques have emerged over the past decades, leaving researchers and analysts with a wide variety of choices for reducing their data, all the more so given some techniques come with additional hyper-parametrization (e.g., t-SNE, UMAP, etc.). Recent studies are showing that people often use dimensionality reduction as a black-box regardless of the specific properties the method itself preserves. Hence, evaluating and comparing 2D embeddings is usually qualitatively decided, by setting embeddings side-by-side and letting human judgment decide which embedding is the best. In this work, we propose a quantitative way of evaluating embeddings, that nonetheless places human perception at the center. We run a comparative study, where we ask people to select “good” and “misleading” views between scatterplots of low-dimensional embeddings of image datasets, simulating the way people usually select embeddings. We use the study data as labels for a set of quality metrics for a supervised machine learning model whose purpose is to discover and quantify what exactly people are looking for when deciding between embeddings. With the model as a proxy for human judgments, we use it to rank embeddings on new datasets, explain why they are relevant, and quantify the degree of subjectivity when people select preferred embeddings.}, author = {Morariu, Cristina and Bibal, Adrien and Cutura, Rene and Fr{\'e}nay, Beno{\^\i}t and Sedlmair, Michael}, doi = {10.1109/TVCG.2022.3209449}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = 1, pages = {745-755}, title = {Predicting User Preferences of Dimensionality Reduction Embedding Quality}, url = {https://ieeexplore.ieee.org/document/9904619}, volume = 29, year = 2023 }
  Link
  https://ieeexplore.ieee.org/document/9904619
2022
1. Tkachev, Gleb ; Cutura, Rene ; Sedlmair, Michael ; Frey, Steffen ; Ertl, Thomas: Metaphorical Visualization: Mapping Data to Familiar Concepts. In: CHI Conference on Human Factors in Computing Systems Extended Abstracts, CHI Conference on Human Factors in Computing Systems Extended Abstracts : ACM, 2022, S. 1–10
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  @inproceedings{Tkachev_2022, abstract = {We present a new approach to visualizing data that is well-suited for personal and casual applications. The idea is to map the data to another dataset that is already familiar to the user, and then rely on their existing knowledge to illustrate relationships in the data. We construct the map by preserving pairwise distances or by maintaining relative values of specific data attributes. This metaphorical mapping is very flexible and allows us to adapt the visualization to its application and target audience. We present several examples where we map data to different domains and representations. This includes mapping data to cat images, encoding research interests with neural style transfer and representing movies as stars in the night sky. Overall, we find that although metaphors are not as accurate as the traditional techniques, they can help design engaging and personalized visualizations.}, author = {Tkachev, Gleb and Cutura, Rene and Sedlmair, Michael and Frey, Steffen and Ertl, Thomas}, booktitle = {{CHI} Conference on Human Factors in Computing Systems Extended Abstracts}, doi = {10.1145/3491101.3516393}, month = {04}, pages = {1-10}, publisher = {{ACM}}, title = {Metaphorical Visualization: Mapping Data to Familiar Concepts}, url = {https://doi.org/10.1145%2F3491101.3516393}, year = 2022 }
  Link
  https://doi.org/10.1145%2F3491101.3516393
2. Angerbauer, Katrin ; Rodrigues, Nils ; Cutura, Rene ; Öney, Seyda ; Pathmanathan, Nelusa ; Morariu, Cristina ; Weiskopf, Daniel ; Sedlmair, Michael: Accessibility for Color Vision Deficiencies: Challenges and Findings of a Large Scale Study on Paper Figures. In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. New Orleans, LA, USA : Association for Computing Machinery, 2022 — ISBN 9781450391573
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  @inproceedings{10.1145/3491102.3502133, abstract = {We present an exploratory study on the accessibility of images in publications when viewed with color vision deficiencies (CVDs). The study is based on 1,710 images sampled from a visualization dataset (VIS30K) over five years. We simulated four CVDs on each image. First, four researchers (one with a CVD) identified existing issues and helpful aspects in a subset of the images. Based on the resulting labels, 200 crowdworkers provided  30,000 ratings on present CVD issues in the simulated images. We analyzed this data for correlations, clusters, trends, and free text comments to gain a first overview of paper figure accessibility. Overall, about 60 % of the images were rated accessible. Furthermore, our study indicates that accessibility issues are subjective and hard to detect. On a meta-level, we reflect on our study experience to point out challenges and opportunities of large-scale accessibility studies for future research directions.}, address = {New York, NY, USA}, articleno = {134}, author = {Angerbauer, Katrin and Rodrigues, Nils and Cutura, Rene and \"{O}ney, Seyda and Pathmanathan, Nelusa and Morariu, Cristina and Weiskopf, Daniel and Sedlmair, Michael}, booktitle = {Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems}, doi = {10.1145/3491102.3502133}, isbn = {9781450391573}, location = {New Orleans, LA, USA}, numpages = {23}, publisher = {Association for Computing Machinery}, series = {CHI '22}, title = {Accessibility for Color Vision Deficiencies: Challenges and Findings of a Large Scale Study on Paper Figures}, url = {https://doi.org/10.1145/3491102.3502133}, year = 2022 }
  Link
  https://doi.org/10.1145/3491102.3502133
2021
1. Rijken, Gerrit J ; Cutura, Rene ; Heyen, Frank ; Sedlmair, Michael ; Correll, Michael ; Dykes, Jason ; Smit, Noeska: Illegible Semantics: Exploring the Design Space of Metal Logos. In: IEEE VIS alt.VIS Workshop, IEEE VIS alt.VIS Workshop, 2021
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  @inproceedings{rijken2021altvis, abstract = {The logos of metal bands can be by turns gaudy, uncouth, or nearly illegible. Yet, these logos work: they communicate sophisticated notions of genre and emotional affect. In this paper we use the design considerations of metal logos to explore the space of "illegible semantics": the ways that text can communicate information at the cost of readability, which is not always the most important objective. In this work, drawing on formative visualization theory, professional design expertise, and empirical assessments of a corpus of metal band logos, we describe a design space of metal logos and present a tool through which logo characteristics can be explored through visualization. We investigate ways in which logo designers imbue their text with meaning and consider opportunities and implications for visualization more widely.}, author = {Rijken, Gerrit J and Cutura, Rene and Heyen, Frank and Sedlmair, Michael and Correll, Michael and Dykes, Jason and Smit, Noeska}, booktitle = {IEEE VIS alt.VIS Workshop}, title = {Illegible Semantics: Exploring the Design Space of Metal Logos}, url = {https://arxiv.org/abs/2109.01688}, year = 2021 }
  Link
  https://arxiv.org/abs/2109.01688
2. Cutura, Rene ; Morariu, Cristina ; Cheng, Zhanglin ; Wang, Yunhai ; Weiskopf, Daniel ; Sedlmair, Michael: Hagrid — Gridify Scatterplots with Hilbert and Gosper Curves. In: The 14th International Symposium on Visual Information Communication and Interaction, The 14th International Symposium on Visual Information Communication and Interaction. Potsdam, Germany : Association for Computing Machinery, 2021 — ISBN 9781450386470, S. 1:1—1:8
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  @inproceedings{10.1145/3481549.3481569, abstract = { A common enhancement of scatterplots represents points as small multiples, glyphs, or thumbnail images. As this encoding often results in overlaps, a general strategy is to alter the position of the data points, for instance, to a grid-like structure. Previous approaches rely on solving expensive optimization problems or on dividing the space that alter the global structure of the scatterplot. To find a good balance between efficiency and neighborhood and layout preservation, we propose Hagrid, a technique that uses space-filling curves (SFCs) to “gridify” a scatterplot without employing expensive collision detection and handling mechanisms. Using SFCs ensures that the points are plotted close to their original position, retaining approximately the same global structure. The resulting scatterplot is mapped onto a rectangular or hexagonal grid, using Hilbert and Gosper curves. We discuss and evaluate the theoretic runtime of our approach and quantitatively compare our approach to three state-of-the-art gridifying approaches, DGrid, Small multiples with gaps SMWG, and CorrelatedMultiples CMDS, in an evaluation comprising 339 scatterplots. Here, we compute several quality measures for neighborhood preservation together with an analysis of the actual runtimes. The main results show that, compared to the best other technique, Hagrid is faster by a factor of four, while achieving similar or even better quality of the gridified layout. Due to its computational efficiency, our approach also allows novel applications of gridifying approaches in interactive settings, such as removing local overlap upon hovering over a scatterplot.}, address = {New York, NY, USA}, articleno = {1}, author = {Cutura, Rene and Morariu, Cristina and Cheng, Zhanglin and Wang, Yunhai and Weiskopf, Daniel and Sedlmair, Michael}, booktitle = {The 14th International Symposium on Visual Information Communication and Interaction}, doi = {10.1145/3481549.3481569}, isbn = {9781450386470}, location = {Potsdam, Germany}, numpages = {8}, pages = {1:1—1:8}, publisher = {Association for Computing Machinery}, series = {VINCI 2021}, title = {Hagrid — Gridify Scatterplots with Hilbert and Gosper Curves}, url = {https://doi.org/10.1145/3481549.3481569}, year = 2021 }
  Link
  https://doi.org/10.1145/3481549.3481569
3. Cutura, Rene ; Angerbauer, Katrin ; Heyen, Frank ; Hube, Natalie ; Sedlmair, Michael: DaRt: Generative Art using Dimensionality Reduction Algorithms. In: 2021 IEEE VIS Arts Program (VISAP), 2021 IEEE VIS Arts Program (VISAP) : IEEE, 2021, S. 59--72
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  @inproceedings{cutura2021generative, abstract = {Dimensionality Reduction (DR) is a popular technique that is often used in Machine Learning and Visualization communities to analyze high-dimensional data. The approach is empirically proven to be powerful for uncovering previously unseen structures in the data. While observing the results of the intermediate optimization steps of DR algorithms, we coincidently discovered the artistic beauty of the DR process. With enthusiasm for the beauty, we decided to look at DR from a generative art lens rather than their technical application aspects and use DR techniques to create artwork. Particularly, we use the optimization process to generate images, by drawing each intermediate step of the optimization process with some opacity over the previous intermediate result. As another alternative input, we used a neural-network model for face-landmark detection, to apply DR to portraits, while maintaining some facial properties, resulting in abstracted facial avatars. In this work, we provide such a collection of such artwork.}, author = {Cutura, Rene and Angerbauer, Katrin and Heyen, Frank and Hube, Natalie and Sedlmair, Michael}, booktitle = {2021 IEEE VIS Arts Program (VISAP)}, doi = {10.1109/VISAP52981.2021.00013}, organization = {IEEE}, pages = {59--72}, title = {DaRt: Generative Art using Dimensionality Reduction Algorithms}, year = 2021 }
4. Morariu, Cristina ; Bibal, Adrien ; Cutura, Rene ; Frenay, Benoit ; Sedlmair, Michael: DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality (Technical Report Nr. arXiv:2105.09275) : arXiv preprint, 2021
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  @techreport{morariu2021arxiv, abstract = {A plethora of dimensionality reduction techniques have emerged over the past decades, leaving researchers and analysts with a wide variety of choices for reducing their data, all the more so given some techniques come with additional parametrization (e.g. t-SNE, UMAP, etc.). Recent studies are showing that people often use dimensionality reduction as a black-box regardless of the specific properties the method itself preserves. Hence, evaluating and comparing 2D projections is usually qualitatively decided, by setting projections side-by-side and letting human judgment decide which projection is the best. In this work, we propose a quantitative way of evaluating projections, that nonetheless places human perception at the center. We run a comparative study, where we ask people to select 'good' and 'misleading' views between scatterplots of low-level projections of image datasets, simulating the way people usually select projections. We use the study data as labels for a set of quality metrics whose purpose is to discover and quantify what exactly people are looking for when deciding between projections. With this proxy for human judgments, we use it to rank projections on new datasets, explain why they are relevant, and quantify the degree of subjectivity in projections selected.}, author = {Morariu, Cristina and Bibal, Adrien and Cutura, Rene and Frenay, Benoit and Sedlmair, Michael}, institution = {{arXiv} preprint}, number = {arXiv:2105.09275}, title = {DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality}, type = {Technical Report}, url = {https://arxiv.org/abs/2105.09275}, year = 2021 }
  Link
  https://arxiv.org/abs/2105.09275
2020
1. Achberger, Alexander ; Cutura, René ; Türksoy, Oguzhan ; Sedlmair, Michael: Caarvida: Visual Analytics for Test Drive Videos. In: Proceedings of the International Conference on Advanced Visual Interfaces, Proceedings of the International Conference on Advanced Visual Interfaces, 2020, S. 1--9
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  @inproceedings{achberger2020caarvida, abstract = {We report on an interdisciplinary visual analytics project wherein automotive engineers analyze test drive videos. These videos are annotated with navigation-specific augmented reality (AR) con�tent, and the engineers need to identify issues and evaluate the behavior of the underlying AR navigation system. With the increas�ing amount of video data, traditional analysis approaches can no longer be conducted in an acceptable timeframe. To address this issue, we collaboratively developed Caarvida, a visual analytics tool that helps engineers to accomplish their tasks faster and handle an increased number of videos. Caarvida combines automatic video analysis with interactive and visual user interfaces. We conducted two case studies which show that Caarvida successfully supports domain experts and speeds up their task completion time.}, author = {Achberger, Alexander and Cutura, René and Türksoy, Oguzhan and Sedlmair, Michael}, booktitle = {Proceedings of the International Conference on Advanced Visual Interfaces}, doi = {10.1145/3399715.3399862}, pages = {1--9}, title = {Caarvida: Visual Analytics for Test Drive Videos}, url = {https://doi.org/10.1145/3399715.3399862}, year = 2020 }
  Link
  https://doi.org/10.1145/3399715.3399862

Rene Cutura

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2023

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2022

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2021

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2020

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Rene Cutura

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2023

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2022

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2021

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2020

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