Marcel Spehr

Visualizing time-dependent key performance indicator in a graph-based analysis

The usage of visual analytics during the analysis of business warehouse calculated key performance indicators is one emerging challenge in modern business applications. On the one hand, a complex network of key performance indicators has to be supervised. On the other hand, within this network only few key performance indicators change obviously within a short period of time. The sole mapping of the complexity of a network of key performance indicators to a graph-based visualization only covers static information and neglects temporal dependencies. We present a new visualization approach for the enrichment of graph-based visualizations of key performance indicator networks by introducing a multi-encoded visualization of additional functional, contextual and temporal information. The should help the user to understand relationships between KPIs and alert him if something is going wrong.

Feature propagation on image webs for enhanced image retrieval

The bag-of-features model is often deployed in content-based image retrieval to measure image similarity. In cases where the visual appearance of semantically similar images differs largely, feature histograms mismatch and the model fails. We increase the robustness of feature histograms by automatically augmenting them with features of related images. We establish image relations by image web construction and adapt a label propagation scheme from the domain of semi-supervised learning for feature augmentation. While the benefit of feature augmentation has been shown before, our approach refrains from the use of semantic labels. Instead we show how to increase the performance of the bag-of-features model substantially on a completely unlabeled image corpus.

Sum-of-superellipses: a low parameter model for amplitude spectra of natural images

Amplitude spectra of natural images look surprisingly alike. Their shape is governed by the famous 1/f power law. In this work we propose a novel low parameter model for describing these spectra. The Sum-of-Superellipses conserves their common falloff behavior while simultaneously capturing the dimensions of variation--concavity, isotropy, slope, main orientation--in a small set of meaningful illustrative parameters. We demonstrate its general usefulness in standard computer vision tasks like scene recognition and image compression.

Wifbs: A Web-Based Image Feature Benchmark System

Automatic analysis of image data is of high importance for many applications. Given an image classification problem one needs three things: (i) Training data and tools to extract (ii) relevant visual information—usually image features—that can be used by (iii) classification algorithms. For given (i), a multitude of candidates present themselves for (ii) and (iii). Model selection becomes the main issue. We present a web-based feature benchmark system enabling system designers to streamline tool-chains to specific needs using available implementations of candidate tools. Our system features a modular architecture, remote and parallel computing, extensibility and—from a user’s standpoint—platform independence due to its web-based nature. Using Wifbs, image features can be subjected to a sophisticated and unbiased model selection procedure to compose optimized pipelines for given image classification problems.

Directed image search with local parallel feature axes

The browsing of large image data bases has become a standard problem not only on the web but also in private photo collections. Most browsing techniques build on high dimensional feature spaces that are reduced to one or two dimensions when presented to the user. As this approach does not scale well with the size of the data base we propose to use an interface based on the concept of parallel coordinates. Around the currently selected image, we collect images for each feature dimension, which vary only in this feature coordinate, and present them to the user in a row by row fashion. In this way the user can understand the individual feature dimensions independently. Besides directed image search the interface is well suited to explore classes of images and furthermore to evaluate how intuitive individual feature dimensions are for the user.

Visuelle Analytik biologischer Daten

Einleitung Biologische Daten sind heterogen, sehr komplex und oft sehr gros. Eine angemessene Visualisierung leistet hierbei einen entscheidenden Beitrag zum Verstandnis der Daten. Die Visualisierung biologischer Daten spielt daher auch eine zentrale Rolle in der Bioinformatik. Hier reichen die Anwendungen von der Visualisierung einzelner Proteine oder ganzer Genome, von Familien vonGenen, evolutionaren Verwandtschaftsverhaltnissen, makromolekularer Strukturen, mikroskopischer Bilddaten bis hin zur Darstellung von metabolischen oder regulatorischen Netzwerken und systembiologischer Daten (siehe Abb. 1 fur einige Beispiele). Aufgrund der zunehmenden Komplexitat und Verbundenheit biologischer Daten (man denke hier insbesondere an systembiologische Daten) ist eine integrative sowie standardisierte Visualisierung und die Entwicklung leistungsstarker und benutzerfreundlicher Werkzeuge von wachsender Bedeutung. Eine zunehmend grose Rolle spielen dabei Werkzeuge, die zudem das Paradigma der visuellen Analytik (engl. ,,visual analytics“) verfolgen. Grundsatzlich integriert die visuelle Analytik Visualisierung, Datenanalyse und Interaktion durch den Menschen. Bei biologischen Daten hat der Einsatz der visuellen Analytik daruber hinaus das Ziel, die komplexen experimentellen Daten in Wissen umzusetzen. Forschern soll ein System zur Verfugung gestellt werden, das es erlaubt, Einsichten in biologische Prozesse in Zellen, Geweben und schlieslich Organismen zu gewinnen sowie eine Modellierung biologischer Systeme vorzunehmen. Die visuelle Analytik biologischer Daten hat erst vor kurzem auf den Information-Visualizationund Visual-Analytics-Konferenzen Beachtung gefunden. Einige auch in wissenschaftlichen Zeitschriften publizierte Artikel sind Themen wie Clustering von Expressionsdaten [17], der GenomAssemblierung [18] oder der Bestimmung von Funktionen von Genen in neu sequenzierten Genomen [20] gewidmet. Die diesjahrige IEEE VAST Challenge stand ganz im Zeichen einer biologischen Fragestellung. Im folgenden Artikel mochten wir insbesondere auf drei der oben genannten Teilgebiete der Visualisierung biologischer Daten genauer eingehen: die visuelle Analytik von Genexpressionsdaten, die Visualisierung biologischer Netzwerke sowie die inhaltsbasierte Suche in zellbiologischen Bilddatenbanken.