Visual feature space analysis SPP 1335


Abstract: Many important data retrieval and analysis tasks such as similarity search, clustering, and classification, require feature vector (descriptor) representations to calculate distances between instances of complex data types. However, it is usually a priori not clear what the best feature descriptor to solve a given application problem is. In most cases, a wealth of options for generating descriptors exists, including choice of feature type, and setting of preprocessing, normalization, and level of detail parameters. Above all, the feature vector descriptors to be employed need to fit the given application domain, data set characteristics, and user task. Only feature vectors appropriately configured to the overall application, user, and data environment will yield satisfactory analysis results. Offline benchmarking on predefined reference data sets is of limited use, as it is data- and application-dependent, and expensive in terms of supervised information required.Located in the fields of data analysis and visualization, this project aims at generically improving the performance of feature-based data analysis applications, by means of novel tools for interactive and user-centered visual feature space analysis. Effective Visual Analytics methodology will be developed to support visual-interactive analysis and configuration of candidate feature vector descriptors, to best fit the given data, application, and user task at hand. Integrated with respective feature-based applications, automatic feature analysis and configuration methods will be combined with appropriate visualization and interaction facilities. Automatic analysis will screen the space of possible feature descriptors, identifying promising candidate features. Visualization of the automatically obtained analysis results, in combination with appropriate user interaction and feedback, will allow converging to a set of feature descriptors best supporting the user task at hand. The Visual Analytics methodology to be developed will be embedded into real-world, relevant types of applications and data.p pbProject PresentationpPresentation Slides from Dagstuhl Seminar on Scalable Visual Analytics, Nov.2010

  • Department of Computer and Information Science
    Bremm, Sebastian; Andrienko, Gennady; Andrienko, Natalia; Schreck, Tobias; Landesberger, Tatiana von (2011): Interactive analysis of object group changes over time EuroVA 2011 : international workshop on visual analytics. 2011. Available under: doi: 10.2312/PE/EuroVAST/EuroVA11/041-044

Interactive analysis of object group changes over time


The analysis of time-dependent data is an important task in various application domains. Often, the analyzed data objects belong to groups. The group memberships may stem from natural arrangements (e.g., animal herds), or may be constructed during analysis (e.g., by clustering). Group membership may change over time. Therefore, one important analytical aspect is to examine these changes (e.g., which herds change members and when). In this paper, we present a technique for visual analysis of group changes over time. We combine their visualization and automatic analysis. Interactive functions allow for tracking the data changes over time on group, set and individual level. We also consider added and removed objects (e.g., newly born or died animals). For large time series, automatic data analysis selects interesting time points and group changes for detailed examination. We apply our approach on the VAST 2008 challenge data set revealing new insights.

Origin (projects)

    Bremm, Sebastian; Landesberger, Tatiana von; Bernard, Jürgen; Schreck, Tobias (2011): Assisted descriptor selection based on visual comparative data analysis Computer Graphics Forum. 2011, 30(3), pp. 891-900. ISSN 0167-7055. Available under: doi: 10.1111/j.1467-8659.2011.01938.x

Assisted descriptor selection based on visual comparative data analysis


Exploration and selection of data descriptors representing objects using a set of features are important components in many data analysis tasks. Usually, for a given dataset, an optimal data description does not exist, as the suitable data representation is strongly use case dependent. Many solutions for selecting a suitable data description have been proposed. In most instances, they require data labels and often are black box approaches. Non-expert users have difficulties to comprehend the coherency of input, parameters, and output of these algorithms. Alternative approaches, interactive systems for visual feature selection, overburden the user with an overwhelming set of options and data views. Therefore, it is essential to offer the users a guidance in this analytical process. In this paper, we present a novel system for data description selection, which facilitates the user’s access to the data analysis process. As finding of suitable data description consists of several steps, we support the user with guidance. Our system combines automatic data analysis with interactive visualizations. By this, the system provides a recommendation for suitable data descriptor selections. It supports the comparison of data descriptors with differing dimensionality for unlabeled data. We propose specialized scores and interactive views for descriptor comparison. The visualization techniques are scatterplot-based and grid-based. For the latter case, we apply Self-Organizing Maps as adaptive grids which are well suited for large multi-dimensional data sets. As an example, we demonstrate the usability of our system on a real-world biochemical application.

Origin (projects)

Funding sources
Name Finanzierungstyp Kategorie Project no.
Schwerpunktprogramm third-party funds research funding program 658/11
Further information
Period: 18.08.2008 – 17.08.2011