Schreck, Tobias
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Guided Sketching for Visual Search and Exploration in Large Scatter Plot Spaces
Recently, there has been an interest in methods for filtering large scatter plot spaces for interesting patterns. However, user interaction remains crucial in starting an explorative analysis in a large scatter plot space. We introduce an approach for explorative search and navigation in large sets of scatter plot diagrams. By means of a sketch-based query interface, users can start the exploration process by providing a visual example of the pattern they are interested in. A shadow-drawing approach provides suggestions for possibly relevant patterns while query drawing takes place, supporting the visual search process. We apply the approach on a large real-world data set, demonstrating the principal functionality and usefulness of our technique.
TimeSeriesPaths : Projection-Based Explorative Analysis of Multivariate Time Series Data
The analysis of time-dependent data is an important problem in many application domains, and interactive visualization of time-series data can help in understanding patterns in large time series data. Many effective approaches already exist for visual analysis of univariate time series supporting tasks such as assessment of data quality, detection of outliers, or identification of periodically or frequently occurring patterns. However, much fewer approaches exist which support multivariate time series. The existence of multiple values per time stamp makes the analysis task per se harder, and existing visualization techniques often do not scale well. We introduce an approach for visual analysis of large multivariate time-dependent data, based on the idea of projecting multivariate measurements to a 2D display, visualizing the time dimension by trajectories. We use visual data aggregation metaphors based on grouping of similar data elements to scale with multivariate time series. Aggregation procedures can either be based on statistical properties of the data or on data clustering routines. Appropriately defined user controls allow to navigate and explore the data and interactively steer the parameters of the data aggregation to enhance data analysis. We present an implementation of our approach and apply it on a comprehensive data set from the field of earth observation, demonstrating the applicability and usefulness of our approach.
Sketch-based 3D Model Retrieval using Keyshapes for Global and Local Representation
Since 3D models are becoming more popular, the need for effective methods capable of retrieving 3D models is becoming crucial. Current methods require an example 3D model as query. However, in many cases, such a query is not easy to get. An alternative is using a hand-drawn sketch as query. In this work, we present a new keyshape based approach named HKO-KASD for retrieving 3D models using rough sketches as queries. Our Approach comprises two general steps. First, a global descriptor is used to determine the appropriate viewpoint for each model. Second, we apply a local matching process to determine the final ranking for an input sketch. To this end, we present a local descriptor capable of working with sketch representations. The global descriptors as well as the local descriptors rely on a set of keyshapes precomputed from 2D representations of 3D models and from the query sketch as well. We evaluate our method using the first-tier precision and compare it with current approaches (HELO, STELA). Our results show a significant increase in precision for many classes of 3D models.
STELA : Sketch-Based 3D Model Retrieval using a Structure-Based Local Approach
Since 3D models are becoming more popular, the need for effective methods capable of retrieving 3D models are becoming crucial. Current methods require an example 3D model as query. However, in many cases, such a query is not easy to get. An alternative is using a hand-draw sketch as query. We present a structure-based local approach (STELA) for retrieving 3D models using a rough sketch as query. It consists of four steps: get an abstract image, detect keyshapes, compute a local descriptor, and match local descriptors. We represent a 3D model by means of suggestive contours. Our proposal includes an additional step aiming at reducing the number of models that will be compared by our local approach. The proposed method is invariant to position, scale, and rotation changes as well. We evaluate our method using the first-tier precision and compare it with a current global approach (HELO). Our results show an increasing in precision for many classes of 3D models.
Visual-Interactive Querying for Multivariate Research Data Repositories Using Bag-of-Words
Large amounts of multivariate data are collected in different areas of scientific research and industrial production. These data are collected, archived and made publicly available by research data repositories. In addition to meta-data based access, content-based approaches are highly desirable to effectively retrieve, discover and analyze data sets of interest. Several such methods, that allow users to search for particular curve progressions, have been proposed. However, a major challenge when providing content-based access -- interactive feedback during query formulation -- has not received much attention yet. This is important because it can substantially improve the user's search effectiveness. In this paper, we present a novel interactive feedback approach for content-based access to multivariate research data. Thereby, we enable query modalities that were not available for multivariate data before. We provide instant search results and highlight query patterns in the result set. Real-time search suggestions give an overview of important patterns to look for in the data repository. For this purpose, we develop a bag-of-words index for multivariate data as the back-end of our approach. We apply our method to a large repository of multivariate data from the climate research domain. We describe a use-case for the discovery of interesting patterns in maritime climate research using our new visual-interactive query tools.
Guided Discovery of Interesting Relationships Between Time Series Clusters and Metadata Properties
Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, especially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.
SHREC’12 Track : Sketch-Based 3D Shape Retrieval
Sketch-based 3D shape retrieval has become an important research topic in content-based 3D object retrieval. The aim of this track is to measure and compare the performance of sketch-based 3D shape retrieval methods implemented by different participants over the world. The track is based on a new sketch-based 3D shape benchmark, which contains two types of sketch queries and two versions of target 3D models. In this track, 7 runs have been submitted by 5 groups and their retrieval accuracies were evaluated using 7 commonly used retrieval performance metrics. We hope that the benchmark, its corresponding evaluation code, and the comparative evaluation results of the state-of-the-art sketch-based 3D model retrieval algorithms will contribute to the progress of this research direction for the 3D model retrieval community.
A Benchmark for Content-Based Retrieval in Bivariate Data Collections
Huge amounts of various research data are produced and made publicly available in digital libraries. An important category is bivariate data (measurements of one variable versus the other). Examples of bivariate data include observations of temperature and ozone levels (e.g., in environmental observation), domestic production and unemployment (e.g., in economics), or education and income level levels (in the social sciences). For accessing these data, content-based retrieval is an important query modality. It allows researchers to search for specific relationships among data variables (e.g., quadratic dependence of temperature on altitude). However, such retrieval is to date a challenge, as it is not clear which similarity measures to apply. Various approaches have been proposed, yet no benchmarks to compare their retrieval effectiveness have been defined. In this paper, we construct a benchmark for retrieval of bivariate data. It is based on a large collection of bivariate research data. To define similarity classes, we use category information that was annotated by domain experts. The resulting similarity classes are used to compare several recently proposed content-based retrieval approaches for bivariate data, by means of precision and recall. This study is the first to present an encompassing benchmark data set and compare the performance of respective techniques. We also identify potential research directions based on the results obtained for bivariate data. The benchmark and implementations of similarity functions are made available, to foster research in this emerging area of content-based retrieval.
Graph-Based Combinations of Fragment Descriptors for Improved 3D Object Retrieval
3D Object Retrieval is an important field of research with many application possibilities. One of the main goals in this research is the development of discriminative methods for similarity search. The descriptor-based approach to date has seen a lot of research attention, with many different extraction algorithms proposed. In previous work, we have introduced a simple but effective scheme for 3D model retrieval based on a spatially fixed combination of 3D object fragment descriptors. In this work, we propose a novel flexible combination scheme based on finding the best matching fragment descriptors to use in the combination. By an exhaustive experimental evaluation on established Benchmark data we show the capability of the new combination scheme to provide improved retrieval effectiveness. The method is proposed as a versatile and inexpensive method to enhance the effectiveness of a given global 3D descriptor approach.
Content-Based Layouts for Exploratory Metadata Search in Scientific Research Data
Today's digital libraries (DLs) archive vast amounts of Information in the form of text, videos, images, data measurements, etc. User access to DL content can rely on similarity between metadata elements, or similarity between the data itself (content-based similarity). We consider the problem of exploratory search in large DLs of time-oriented data. We propose a novel approach for overview- rst exploration of data collections based on user-selected metadata properties. In a 2D layout representing entities of the selected property are laid out based on their similarity with respect to the underlying data content. The display is enhanced by compact summarizations of underlying data elements, and forms the basis for exploratory navigation of users in the data space. The approach is proposed as an interface for visual exploration, leading the user to discover interesting relationships between data items relying on content-based similarity between data items and their respective metadata labels. We apply the method on real data sets from the earth Observation community, showing its applicability and usefulness.