Fraunhofer Institute for Intelligent Analysis and Information Systems

Germany

Fraunhofer Institute for Intelligent Analysis and Information Systems

Germany

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Kisilevich S.,University of Konstanz | Andrienko G.,Fraunhofer Institute for Intelligent Analysis and Information Systems
Proceedings of the International Conference on Information Visualisation | Year: 2010

Photo-sharing websites such as Flickr and Panoramio contain millions of geotagged images contributed by people from all over the world. Characteristics of these data pose new challenges in the domain of spatio-temporal analysis. In this paper, we define several different tasks related to analysis of attractive places, points of interest and comparison of behavioral patterns of different user communities on geotagged photo data. We perform analysis and comparison of temporal events, rankings of sightseeing places in a city, and study mobility of people using geotagged photos. We take a systematic approach to accomplish these tasks by applying scalable computational techniques, using statistical and data mining algorithms, combined with interactive geo-visualization. We provide exploratory visual analysis environment, which allows the analyst to detect spatial and temporal patterns and extract additional knowledge from large geotagged photo collections. We demonstrate our approach by applying the methods to several regions in the world. © 2010 IEEE.


Evangelidis G.D.,French Institute for Research in Computer Science and Automation | Bauckhage C.,Fraunhofer Institute for Intelligent Analysis and Information Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2013

This paper addresses the problem of video alignment. We present efficient approaches that allow for spatiotemporal alignment of two sequences. Unlike most related works, we consider independently moving cameras that capture a 3D scene at different times. The novelty of the proposed method lies in the adaptation and extension of an efficient information retrieval framework that casts the sequences as an image database and a set of query frames, respectively. The efficient retrieval builds on the recently proposed quad descriptor. In this context, we define the 3D Vote Space (VS) by aggregating votes through a multiquerying (multiscale) scheme and we present two solutions based on VS entries; a causal solution that permits online synchronization and a global solution through multiscale dynamic programming. In addition, we extend the recently introduced ECC image-alignment algorithm to the temporal dimension that allows for spatial registration and synchronization refinement with subframe accuracy. We investigate full search and quantization methods for short descriptors and we compare the proposed schemes with the state of the art. Experiments with real videos by moving or static cameras demonstrate the efficiency of the proposed method and verify its effectiveness with respect to spatiotemporal alignment accuracy. © 1979-2012 IEEE.


Ruping S.,Fraunhofer Institute for Intelligent Analysis and Information Systems
Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz | Year: 2015

Healthcare is one of the business fields with the highest Big Data potential. According to the prevailing definition, Big Data refers to the fact that data today is often too large and heterogeneous and changes too quickly to be stored, processed, and transformed into value by previous technologies. The technological trends drive Big Data: business processes are more and more executed electronically, consumers produce more and more data themselves − e.g. in social networks − and finally ever increasing digitalization. Currently, several new trends towards new data sources and innovative data analysis appear in medicine and healthcare. From the research perspective, omics-research is one clear Big Data topic. In practice, the electronic health records, free open data and the “quantified self” offer new perspectives for data analytics. Regarding analytics, significant advances have been made in the information extraction from text data, which unlocks a lot of data from clinical documentation for analytics purposes. At the same time, medicine and healthcare is lagging behind in the adoption of Big Data approaches. This can be traced to particular problems regarding data complexity and organizational, legal, and ethical challenges. The growing uptake of Big Data in general and first best-practice examples in medicine and healthcare in particular, indicate that innovative solutions will be coming. This paper gives an overview of the potentials of Big Data in medicine and healthcare. © 2015, Springer-Verlag Berlin Heidelberg.


Muhlenbein H.,Fraunhofer Institute for Intelligent Analysis and Information Systems
Adaptation, Learning, and Optimization | Year: 2012

Estimation of Distribution Algorithms (EDAs) have been proposed as an extension of genetic algorithms.We assume that the function to be optimized is additively decomposed (ADF). The interaction graph of the ADF function is used to create exact or approximate factorizations of the Boltzmann distribution. Convergence of the algorithmMN-GIBBS is proven.MN-GIBBS uses a Markov network easily derived from the ADF and Gibbs sampling. We discuss different variants of Gibbs sampling. We show that a good approximation of the true distribution is not necessary, it suffices to use a factorization where the global optima have a large enough probability. This explains the success of EDAs in practical applications using Bayesian networks. © Springer-Verlag Berlin Heidelberg 2012.


Baum D.,Fraunhofer Institute for Intelligent Analysis and Information Systems
Speech Communication | Year: 2012

We investigate how a speaker's preference for specific topics can be used for speaker identification. In domains like broadcast news or parliamentary speeches, speakers have a field of expertise they are associated with. We explore how topic information for a segment of speech, extracted from an automatic speech recognition transcript, can be employed to identify the speaker. Two methods for modelling topic preferences are compared: implicitly, based on speaker-characteristic keywords, and explicitly, by using automatically derived topic models to assign topics to the speech segments. In the keyword-based approach, the segments' tf-idf vectors are classified with Support Vector Machine speaker models. For the topic-model-based approach, a domain-specific topic model is used to represent each segment as a mixture of topics; the speakers' score is derived from the Kullback-Leibler divergence between the topic mixtures of their training data and of the segment. The methods were tested on political speeches given in German parliament by 235 politicians. We found that topic cues do carry speaker information, as the topic-model-based system yielded an equal error rate (EER) of 16.3%. The topic-based approach combined well with a spectral baseline system, improving the EER from 8.6% for the spectral to 6.2% for the fused system. © 2012 Elsevier B.V. All rights reserved.


Andrienko N.,Fraunhofer Institute for Intelligent Analysis and Information Systems | Andrienko G.,Fraunhofer Institute for Intelligent Analysis and Information Systems
Information Visualization | Year: 2013

Analysis of movement is currently a hot research topic in visual analytics. A wide variety of methods and tools for analysis of movement data has been developed in recent years. They allow analysts to look at the data from different perspectives and fulfil diverse analytical tasks. Visual displays and interactive techniques are often combined with computational processing, which, in particular, enables analysis of a larger number of data than would be possible with purely visual methods. Visual analytics leverages methods and tools developed in other areas related to data analytics, particularly statistics, machine learning and geographic information science. We present an illustrated structured survey of the state of the art in visual analytics concerning the analysis of movement data. Besides reviewing the existing works, we demonstrate, using examples, how different visual analytics techniques can support our understanding of various aspects of movement. © The Author(s) 2012.


Rueping S.,Fraunhofer Institute for Intelligent Analysis and Information Systems
ICML 2010 - Proceedings, 27th International Conference on Machine Learning | Year: 2010

A learning problem that has only recently gained attention in the machine learning community is that of learning a classifier from group probabilities. It is a learning task that lies somewhere between the well-known tasks of supervised and unsupervised learning, in the sense that for a set of observations we do not know the labels, but for some groups of observations, the frequency distribution of the label is known. This learning problem has important practical applications, for example in privacy-preserving data mining. This paper presents an approach to learn a classifier from group probabilities based on support vector regression and the idea of inverting a classifier calibration process. A detailed analysis will show that this new approach outperforms existing approaches. Copyright 2010 by the author(s)/owner(s).


Andrienko N.,Fraunhofer Institute for Intelligent Analysis and Information Systems | Andrienko G.,Fraunhofer Institute for Intelligent Analysis and Information Systems
Data Mining and Knowledge Discovery | Year: 2013

To support analysis and modelling of large amounts of spatio-temporal data having the form of spatially referenced time series (TS) of numeric values, we combine interactive visual techniques with computational methods from machine learning and statistics. Clustering methods and interactive techniques are used to group TS by similarity. Statistical methods for TS modelling are then applied to representative TS derived from the groups of similar TS. The framework includes interactive visual interfaces to a library of modelling methods supporting the selection of a suitable method, adjustment of model parameters, and evaluation of the models obtained. The models can be externally stored, communicated, and used for prediction and in further computational analyses. From the visual analytics perspective, the framework suggests a way to externalize spatio-temporal patterns emerging in the mind of the analyst as a result of interactive visual analysis: the patterns are represented in the form of computer-processable and reusable models. From the statistical analysis perspective, the framework demonstrates how TS analysis and modelling can be supported by interactive visual interfaces, particularly, in a case of numerous TS that are hard to analyse individually. From the application perspective, the framework suggests a way to analyse large numbers of spatial TS with the use of well-established statistical methods for TS analysis. © 2012 The Author(s).


Krausz B.,Fraunhofer Institute for Intelligent Analysis and Information Systems | Bauckhage C.,Fraunhofer Institute for Intelligent Analysis and Information Systems
Computer Vision and Image Understanding | Year: 2012

On July 24, 2010, 21 people died and more than 500 were injured in a stampede at the Loveparade, a music festival, in Duisburg, Germany. Although this tragic incident is but one among many terrible crowd disasters that occur during pilgrimage, sports events, or other mass gatherings, it stands out for it has been well documented: there were a total of seven security cameras monitoring the Loveparade and the chain of events that led to disaster was meticulously reconstructed. In this paper, we present an automatic, video-based analysis of the events in Duisburg. While physical models and simulations of human crowd behavior have been reported before, to the best of our knowledge, automatic vision systems that detect congestions and dangerous crowd turbulences in real world settings were not reported yet. Derived from lessons learned from the video footage of the Loveparade, our system is able to detect motion patterns that characterize crowd behavior in stampedes. Based on our analysis, we propose methods for the detection and early warning of dangerous situations during mass events. Since our approach mainly relies on optical flow computations, it runs in real-time and preserves privacy of the people being monitored. © 2011 Elsevier Inc. All rights reserved.


Adrienko N.,Fraunhofer Institute for Intelligent Analysis and Information Systems | Adrienko G.,Fraunhofer Institute for Intelligent Analysis and Information Systems
IEEE Transactions on Visualization and Computer Graphics | Year: 2011

Movement data (trajectories of moving agents) are hard to visualize: numerous intersections and overlapping between trajectories make the display heavily cluttered and illegible. It is necessary to use appropriate data abstraction methods. We suggest a method for spatial generalization and aggregation of movement data, which transforms trajectories into aggregate flows between areas. It is assumed that no predefined areas are given. We have devised a special method for partitioning the underlying territory into appropriate areas. The method is based on extracting significant points from the trajectories. The resulting abstraction conveys essential characteristics of the movement. The degree of abstraction can be controlled through the parameters of the method. We introduce local and global numeric measures of the quality of the generalization, and suggest an approach to improve the quality in selected parts of the territory where this is deemed necessary. The suggested method can be used in interactive visual exploration of movement data and for creating legible flow maps for presentation purposes. © 2011 IEEE.

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