PUMA publications for /user/qihan/2018%20visus:hanqihttps://puma.ub.uni-stuttgart.de/user/qihan/2018%20visus:hanqiPUMA RSS feed for /user/qihan/2018%20visus:hanqi2024-03-29T03:33:12+01:00LabelTransfer - Integrating Static and Dynamic Label Representation for Focus+Context Text Explorationhttps://puma.ub.uni-stuttgart.de/bibtex/20bd696386e23f67ead151622bc3118f5/qihanqihan2018-12-16T17:05:43+01:002018 vis(us) vis-gissend:unibiblio visus:ertl visus:hanqi visus:johnms visus:kochsn <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Qi Han" itemprop="url" href="/person/1a015dbf4e4f05a5f1f6113ab1726bec2/author/0"><span itemprop="name">Q. Han</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Markus John" itemprop="url" href="/person/1a015dbf4e4f05a5f1f6113ab1726bec2/author/1"><span itemprop="name">M. John</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steffen Koch" itemprop="url" href="/person/1a015dbf4e4f05a5f1f6113ab1726bec2/author/2"><span itemprop="name">S. Koch</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ivan Assenov" itemprop="url" href="/person/1a015dbf4e4f05a5f1f6113ab1726bec2/author/3"><span itemprop="name">I. Assenov</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Thomas Ertl" itemprop="url" href="/person/1a015dbf4e4f05a5f1f6113ab1726bec2/author/4"><span itemprop="name">T. Ertl</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA)</span>, </em></span><em>page <span itemprop="pagination">1-8</span>. </em><em><span itemprop="publisher">IEEE</span>, </em>(<em><span>October 2018<meta content="October 2018" itemprop="datePublished"/></span></em>)</span>Sun Dec 16 17:05:43 CET 20182018 International Symposium on Big Data Visual and Immersive Analytics (BDVA)oct1-8LabelTransfer - Integrating Static and Dynamic Label Representation for Focus+Context Text Exploration20182018 vis(us) vis-gissend:unibiblio visus:ertl visus:hanqi visus:johnms visus:kochsn In recent years, interactive visualization to analyze text documents has gained an impressive momentum. This is not surprising considering the fast increase of electronically available textual documents of various kinds. These include, for example, patents, scholarly documents, social media messages, and many other sources that contain valuable knowledge and insights for many stakeholders. Interactive text visualization turned out to be an important means for exploring and gaining insights into complex and often large document collections. An established visualization strategy to represent such collections is using projection-based techniques that visualize documents as glyphs in a 2D view aiming to reflect the semantic similarity of documents by the proximity of their placement. Static labels have been suggested to characterize the overall topics contained in the projected data to improve the effectiveness of such visualization techniques. Other approaches employ magic lenses that enable users to explore the 2D spatialization freely on various granularity levels. In this work, we propose a visual exploration approach that combines cluster-based labeling of projected documents with an interaction concept for magic lens techniques. We offer a set of novel interactive features to support a smooth transition between static labels and the magic lens approach while exploiting the different levels of visual abstraction of both techniques without introducing additional clutter through overdraw. Finally, we provide insights gained from a preliminary user study and present the benefits of our approach.Visual Interactive Labeling of Large Multimedia News Corporahttps://puma.ub.uni-stuttgart.de/bibtex/2d8744b00cedee1bcc77630dc7675f505/qihanqihan2018-12-16T20:33:07+01:002018 vis(us) vis-gis visus:ertl visus:hanqi visus:johnms visus:kurzhako <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Qi Han" itemprop="url" href="/person/1101690e7d39347fd94744ccea7da9f16/author/0"><span itemprop="name">Q. Han</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Markus John" itemprop="url" href="/person/1101690e7d39347fd94744ccea7da9f16/author/1"><span itemprop="name">M. John</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Kuno Kurzhals" itemprop="url" href="/person/1101690e7d39347fd94744ccea7da9f16/author/2"><span itemprop="name">K. Kurzhals</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Johannes Messner" itemprop="url" href="/person/1101690e7d39347fd94744ccea7da9f16/author/3"><span itemprop="name">J. Messner</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Thomas Ertl" itemprop="url" href="/person/1101690e7d39347fd94744ccea7da9f16/author/4"><span itemprop="name">T. Ertl</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">2018 Leipzig Symposium on Visualization in Applications</span>, </em></span>(<em><span>October 2018<meta content="October 2018" itemprop="datePublished"/></span></em>)</span>Sun Dec 16 20:33:07 CET 20182018 Leipzig Symposium on Visualization in ApplicationsoctVisual Interactive Labeling of Large Multimedia News Corpora20182018 vis(us) vis-gis visus:ertl visus:hanqi visus:johnms visus:kurzhako 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.