Relevant bibliographies by topics / Visual Interactive Labeling

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Visual Interactive Labeling'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Visual Interactive Labeling"

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Bernard, Jürgen, Matthias Zeppelzauer, Michael Sedlmair, and Wolfgang Aigner. "VIAL: a unified process for visual interactive labeling." Visual Computer 34, no. 9 (March 19, 2018): 1189–207. http://dx.doi.org/10.1007/s00371-018-1500-3.

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Bernard, Jürgen, Eduard Dobermann, Anna Vögele, Björn Krüger, Jörn Kohlhammer, and Dieter Fellner. "Visual-Interactive Semi-Supervised Labeling of Human Motion Capture Data." Electronic Imaging 2017, no. 1 (January 29, 2017): 34–45. http://dx.doi.org/10.2352/issn.2470-1173.2017.1.vda-387.

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Bernard, Jurgen, Marco Hutter, Matthias Zeppelzauer, Dieter Fellner, and Michael Sedlmair. "Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study." IEEE Transactions on Visualization and Computer Graphics 24, no. 1 (January 2018): 298–308. http://dx.doi.org/10.1109/tvcg.2017.2744818.

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Sevastjanova, Rita, Wolfgang Jentner, Fabian Sperrle, Rebecca Kehlbeck, Jürgen Bernard, and Mennatallah El-assady. "QuestionComb: A Gamification Approach for the Visual Explanation of Linguistic Phenomena through Interactive Labeling." ACM Transactions on Interactive Intelligent Systems 11, no. 3-4 (December 31, 2021): 1–38. http://dx.doi.org/10.1145/3429448.

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Linguistic insight in the form of high-level relationships and rules in text builds the basis of our understanding of language. However, the data-driven generation of such structures often lacks labeled resources that can be used as training data for supervised machine learning. The creation of such ground-truth data is a time-consuming process that often requires domain expertise to resolve text ambiguities and characterize linguistic phenomena. Furthermore, the creation and refinement of machine learning models is often challenging for linguists as the models are often complex, in-transparent, and difficult to understand. To tackle these challenges, we present a visual analytics technique for interactive data labeling that applies concepts from gamification and explainable Artificial Intelligence (XAI) to support complex classification tasks. The visual-interactive labeling interface promotes the creation of effective training data. Visual explanations of learned rules unveil the decisions of the machine learning model and support iterative and interactive optimization. The gamification-inspired design guides the user through the labeling process and provides feedback on the model performance. As an instance of the proposed technique, we present QuestionComb , a workspace tailored to the task of question classification (i.e., in information-seeking vs. non-information-seeking questions). Our evaluation studies confirm that gamification concepts are beneficial to engage users through continuous feedback, offering an effective visual analytics technique when combined with active learning and XAI.
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Mote, Kevin. "Fast Point-Feature Label Placement for Dynamic Visualizations." Information Visualization 6, no. 4 (December 2007): 249–60. http://dx.doi.org/10.1057/palgrave.ivs.9500163.

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This paper describes a fast approach to automatic point label de-confliction on interactive maps. The general Map Labeling problem is NP-hard and has been the subject of much study for decades. Computerized maps have introduced interactive zooming and panning, which has intensified the problem. Providing dynamic labels for such maps typically requires a time-consuming pre-processing phase. In the realm of visual analytics, however, the labeling of interactive maps is further complicated by the use of massive datasets laid out in arbitrary configurations, thus rendering reliance on a pre-processing phase untenable. This paper offers a method for labeling point-features on dynamic maps in real time without pre-processing. The algorithm presented is efficient, scalable, and exceptionally fast; it can label interactive charts and diagrams at speeds of multiple frames per second on maps with tens of thousands of nodes. To accomplish this, the algorithm employs a novel geometric de-confliction approach, the ‘trellis strategy,’ along with a unique label candidate cost analysis to determine the “least expensive” label configuration. The speed and scalability of this approach make it well-suited for visual analytic applications.
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Fan, Xin, Chenlu Li, Xiaoru Yuan, Xiaoju Dong, and Jie Liang. "An interactive visual analytics approach for network anomaly detection through smart labeling." Journal of Visualization 22, no. 5 (September 3, 2019): 955–71. http://dx.doi.org/10.1007/s12650-019-00580-7.

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Hilasaca, Liz Huancapaza, Milton Cezar Ribeiro, and Rosane Minghim. "Visual Active Learning for Labeling: A Case for Soundscape Ecology Data." Information 12, no. 7 (June 29, 2021): 265. http://dx.doi.org/10.3390/info12070265.

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Labeling of samples is a recurrent and time-consuming task in data analysis and machine learning and yet generally overlooked in terms of visual analytics approaches to improve the process. As the number of tailored applications of learning models increases, it is crucial that more effective approaches to labeling are developed. In this paper, we report the development of a methodology and a framework to support labeling, with an application case as background. The methodology performs visual active learning and label propagation with 2D embeddings as layouts to achieve faster and interactive labeling of samples. The framework is realized through SoundscapeX, a tool to support labeling in soundscape ecology data. We have applied the framework to a set of audio recordings collected for a Long Term Ecological Research Project in the Cantareira-Mantiqueira Corridor (LTER CCM), localized in the transition between northeastern São Paulo state and southern Minas Gerais state in Brazil. We employed a pre-label data set of groups of animals to test the efficacy of the approach. The results showed the best accuracy at 94.58% in the prediction of labeling for birds and insects; and 91.09% for the prediction of the sound event as frogs and insects.
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Bernard, Jürgen, Marco Hutter, Michael Sedlmair, Matthias Zeppelzauer, and Tamara Munzner. "A Taxonomy of Property Measures to Unify Active Learning and Human-centered Approaches to Data Labeling." ACM Transactions on Interactive Intelligent Systems 11, no. 3-4 (December 31, 2021): 1–42. http://dx.doi.org/10.1145/3439333.

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Strategies for selecting the next data instance to label, in service of generating labeled data for machine learning, have been considered separately in the machine learning literature on active learning and in the visual analytics literature on human-centered approaches. We propose a unified design space for instance selection strategies to support detailed and fine-grained analysis covering both of these perspectives. We identify a concise set of 15 properties, namely measureable characteristics of datasets or of machine learning models applied to them, that cover most of the strategies in these literatures. To quantify these properties, we introduce Property Measures (PM) as fine-grained building blocks that can be used to formalize instance selection strategies. In addition, we present a taxonomy of PMs to support the description, evaluation, and generation of PMs across four dimensions: machine learning (ML) Model Output , Instance Relations , Measure Functionality , and Measure Valence . We also create computational infrastructure to support qualitative visual data analysis: a visual analytics explainer for PMs built around an implementation of PMs using cascades of eight atomic functions. It supports eight analysis tasks, covering the analysis of datasets and ML models using visual comparison within and between PMs and groups of PMs, and over time during the interactive labeling process. We iteratively refined the PM taxonomy, the explainer, and the task abstraction in parallel with each other during a two-year formative process, and show evidence of their utility through a summative evaluation with the same infrastructure. This research builds a formal baseline for the better understanding of the commonalities and differences of instance selection strategies, which can serve as the stepping stone for the synthesis of novel strategies in future work.
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FAN, N., and CHENG JIN. "GEOMETRIC INVARIANTS CONSTRUCTION FOR SEMANTIC SCENE UNDERSTANDING FROM MULTIPLE VIEWS INSPIRED BY THE HUMAN VISUAL SYSTEM." International Journal of Image and Graphics 12, no. 02 (April 2012): 1250012. http://dx.doi.org/10.1142/s021946781250012x.

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Semantic scene understanding is one of the several significant goals of robotics. In this paper, we propose a framework that is able to construct geometric invariants for simultaneous object detection and segmentation using a simple pairwise interactive context term, for the sake of achieving a preliminary milestone of Semantic scene understanding. The context is incorporated as pairwise interactions between pixels, imposing a prior on the labeling. Our model formulates the multi-class image segmentation task as an energy minimization problem and finds a globally optimal solution using belief propagation and neural network. We experimentally evaluate the proposed method on three publicly available datasets: the MSRC-1, the CorelB datasets, and the PASCAL VOC database. Results show the applicability and efficacy of the proposed method to the multi-class segmentation problem.
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Hur, Cinyoung, JeongA Wi, and YoungBin Kim. "Facilitating the Development of Deep Learning Models with Visual Analytics for Electronic Health Records." International Journal of Environmental Research and Public Health 17, no. 22 (November 10, 2020): 8303. http://dx.doi.org/10.3390/ijerph17228303.

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Electronic health record (EHR) data are widely used to perform early diagnoses and create treatment plans, which are key areas of research. We aimed to increase the efficiency of iteratively applying data-intensive technology and verifying the results for complex and big EHR data. We used a system entailing sequence mining, interpretable deep learning models, and visualization on data extracted from the MIMIC-IIIdatabase for a group of patients diagnosed with heart disease. The results of sequence mining corresponded to specific pathways of interest to medical staff and were used to select patient groups that underwent these pathways. An interactive Sankey diagram representing these pathways and a heat map visually representing the weight of each variable were developed for temporal and quantitative illustration. We applied the proposed system to predict unplanned cardiac surgery using clinical pathways determined by sequence pattern mining to select cardiac surgery from complex EHRs to label subject groups and deep learning models. The proposed system aids in the selection of pathway-based patient groups, simplification of labeling, and exploratory the interpretation of the modeling results. The proposed system can help medical staff explore various pathways that patients have undergone and further facilitate the testing of various clinical hypotheses using big data in the medical domain.
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Dissertations / Theses on the topic "Visual Interactive Labeling"

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Nilsson, Olof. "Visualization of live search." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-102448.

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The classical search engine result page is used for many interactions with search results. While these are effective at communicating relevance, they do not present the context well. By giving the user an overview in the form of a spatialized display, in a domain that has a physical analog that the user is familiar with, context should become pre-attentive and obvious to the user. A prototype has been built that takes public medical information articles and assigns these to parts of the human body. The articles are indexed and made searchable. A visualization presents the coverage of a query on the human body and allows the user to interact with it to explore the results. Through usage cases the function and utility of the approach is shown.
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Han, Qi, Markus John, Kuno Kurzhals, Johannes Messner, and Thomas Ertl. "Visual Interactive Labeling of Large Multimedia News Corpora." 2018. https://ul.qucosa.de/id/qucosa%3A32804.

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The semantic annotation of large multimedia corpora is essential for numerous tasks. Be it for the training of classification algorithms, efficient content retrieval, or for analytical reasoning, appropriate labels are often the first necessity before automatic processing becomes efficient. However, manual labeling of large datasets is time-consuming and tedious. Hence, we present a new visual approach for labeling and retrieval of reports in multimedia news corpora. It combines automatic classifier training based on caption text from news reports with human interpretation to ease the annotation process. In our approach, users can initialize labels with keyword queries and iteratively annotate examples to train a classifier. The proposed visualization displays representative results in an overview that allows to follow different annotation strategies (e.g., active learning) and assess the quality of the classifier. Based on a usage scenario, we demonstrate the successful application of our approach. Therein, users label several topics which interest them and retrieve related documents with high confidence from three years of news reports.
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Conference papers on the topic "Visual Interactive Labeling"

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Kuebert, Thomas, Henning Puder, and Heinz Koeppl. "Daily Routine Recognition with Visual Interactive Labeling by Fusing Acceleration and Audio Signals." In 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). IEEE, 2019. http://dx.doi.org/10.1109/isspit47144.2019.9001791.

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Journal articles
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