Prof. Dr.-Ing.

Bauckhage, Christian

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Now showing 1 - 10 of 43
  • Publication
    Matrix- and Tensor Factorization for Game Content Recommendation
    ( 2020)
    Sifa, Rafet
    ;
    Yawar, Raheel
    ;
    Ramamurthy, Rajkumar
    ;
    Bauckhage, Christian
    ;
    Kersting, Kristian
    Commercial success of modern freemium games hinges on player satisfaction and retention. This calls for the customization of game content or game mechanics in order to keep players engaged. However, whereas game content is already frequently generated using procedural content generation, methods that can reliably assess what kind of content suits a players skills or preferences are still few and far between. Addressing this challenge, we propose novel recommender systems based on latent factor models that allow for recommending quests in a single player role-playing game. In particular, we introduce a tensor factorization algorithm to decompose collections of bipartite matrices which represent how players interests and behaviors change over time. Extensive online bucket type tests during the ongoing operation of a commercial game reveal that our system is able to recommend more engaging quests and to retain more players than previous handcrafted or collaborative filtering approaches.
  • Publication
    Automated identification of sugar beet diseases using smartphones
    ( 2018)
    Hallau, L.
    ;
    Neumann, Marion
    ;
    Klatt, B.
    ;
    Kleinhenz, B.
    ;
    Klein, T.
    ;
    Kuhn, C.
    ;
    Röhrig, M.
    ;
    Bauckhage, Christian
    ;
    Kersting, Kristian
    ;
    Mahlein, A.-K.
    ;
    Steiner, U.
    ;
    Oerke, E.-C.
    Cercospora leaf spot (CLS) poses a high economic risk to sugar beet production due to its potential to greatly reduce yield and quality. For successful integrated management of CLS, rapid and accurate identification of the disease is essential. Diagnosis on the basis of typical visual symptoms is often compromised by the inability to differentiate CLS symptoms from similar symptoms caused by other foliar pathogens of varying significance, or from abiotic stress. An automated detection and classification of CLS and other leaf diseases, enabling a reliable basis for decisions in disease control, would be an alternative to visual as well as molecular and serological methods. This paper presents an algorithm based on a RGB‐image database captured with smartphone cameras for the identification of sugar beet leaf diseases. This tool combines image acquisition and segmentation on the smartphone and advanced image data processing on a server, based on texture features using colour, intensity and gradient values. The diseases are classified using a support vector machine with radial basis function kernel. The algorithm is suitable for binary‐class and multi‐class classification approaches, i.e. the separation between diseased and non‐diseased, and the differentiation among leaf diseases and non‐infected tissue. The classification accuracy for the differentiation of CLS, ramularia leaf spot, phoma leaf spot, beet rust and bacterial blight was 82%, better than that of sugar beet experts classifying diseases from images. However, the technology has not been tested by practitioners. This tool can be adapted to other crops and their diseases and may contribute to improved decision‐making in integrated disease control.
  • Publication
    Neural conditional gradients
    ( 2018)
    Schramowski, Patrick
    ;
    Bauckhage, Christian
    ;
    Kersting, Kristian
    The move from hand-designed to learned optimizers in machine learning has been quite successful for gradient-based and -free optimizers. When facing a constrained problem, however, maintaining feasibility typically requires a projection step, which might be computationally expensive and not differentiable. We show how the design of projection-free convex optimization algorithms can be cast as a learning problem based on Frank-Wolfe Networks: recurrent networks implementing the Frank-Wolfe algorithm aka. conditional gradients. This allows them to learn to exploit structure when, e.g., optimizing over rank-1 matrices. Our LSTM-learned optimizers outperform hand-designed as well learned but unconstrained ones. We demonstrate this for training support vector machines and softmax classifiers.
  • Publication
    Simplex Volume Maximization (SiVM): A matrix factorization algorithm with non-negative constrains and low computing demands for the interpretation of full spectral X-ray fluorescence imaging data
    ( 2017)
    Alfeld, M.
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    Wahabzada, Mirwaes
    ;
    Bauckhage, Christian
    ;
    Kersting, Kristian
    ;
    Snickt, G. van der
    ;
    Noble, P.
    ;
    Janssens, K.
    ;
    Wellenreuther, G.
    ;
    Falkenberg, G.
    Technological progress allows for an ever-faster acquisition of hyperspectral data, challenging the users to keep up with interpreting the recorded data. Matrix factorization, the representation of data sets by bases (or loads) and coefficient (or score) images is long used to support the interpretation of complex data sets. We propose in this publication Simplex Volume Maximization (SiVM) for the analysis of X-ray fluorescence (XRF) imaging data sets. SiVM selects archetypical data points that represents the data set and thus provides easily understandable bases, preserves the non-negative character of XRF data sets and has low demands concerning computing resources. We apply SiVM on an XRF data set of Hans Memling's Portrait of a man from the Lespinette family from the collection of the Mauritshuis (The Hague, NL) and discuss capabilities and shortcomings of SiVM.
  • Publication
    Cell phone image-based plant disease classification
    ( 2016)
    Neumann, Marion
    ;
    Hallau, L.
    ;
    Klatt, B.
    ;
    Kersting, Kristian
    ;
    Bauckhage, Christian
    Modern communication and sensor technology coupled with powerful pattern recognition algorithms for information extraction and classification allow the development and use of integrated systems to tackle environmental problems. This integration is particularly promising for applications in crop farming, where such systems can help to control growth and improve yields while harmful environmental impacts are minimized. Thus, the vision of sustainable agriculture for anybody, anytime, and anywhere in the world can be put into reach. This chapter reviews and presents approaches to plant disease classification based on cell phone images, a novel way to supply farmers with personalized information and processing recommendations in real time. Several statistical image features and a novel scheme of measuring local textures of leaf spots are introduced. The classification of disease symptoms caused by various fungi or bacteria are evaluated for two important agricultural crop varieties, wheat and sugar beet.
  • Publication
    Propagation kernels: Efficient graph kernels from propagated information
    ( 2016)
    Neumann, Marion
    ;
    Garnett, R.
    ;
    Bauckhage, Christian
    ;
    Kersting, Kristian
    We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how information spreads through a set of given graphs. They leverage early-stage distributions from propagation schemes such as random walks to capture structural information encoded in node labels, attributes, and edge information. This has two benefits. First, off-the-shelf propagation schemes can be used to naturally construct kernels for many graph types, including labeled, partially labeled, unlabeled, directed, and attributed graphs. Second, by leveraging existing efficient and informative propagation schemes, propagation kernels can be considerably faster than state-of-the-art approaches without sacrificing predictive performance. We will also show that if the graphs at hand have a regular structure, for instance when modeling image or video data, one can exploit this regularity to scale the kernel computation to large databases of graphs with thousands of nodes. We support our contributions by exhaustive experiments on a number of real-world graphs from a variety of application domains.
  • Publication
    Feeding the world with big data: Uncovering spectral characteristics and dynamics of stressed plants
    ( 2016)
    Kersting, Kristian
    ;
    Bauckhage, Christian
    ;
    Wahabzada, Mirwaes
    ;
    Mahlein, A.-K.
    ;
    Steiner, U.
    ;
    Oerke, E.-C.
    ;
    Römer, C.
    ;
    Plümer, Lutz
    Modern communication, sensing, and actuator technologies as well as methods from signal processing, pattern recognition, and data mining are increasingly applied in agriculture, ultimately helping to meet the challenge of ""How to feed a hungry world?"" Developments such as increased mobility, wireless networks, new environmental sensors, robots, and the computational cloud put the vision of a sustainable agriculture for anybody, anytime, and anywhere within reach. Unfortunately, data-driven agriculture also presents unique computational problems in scale and interpretability: (1) Data is gathered often at massive scale, and (2) researchers and experts of complementary skills have to cooperate in order to develop models and tools for data intensive discovery that yield easy-to-interpret insights for users that are not necessarily trained computer scientists.
  • Publication
    Non-negative matrix factorization for the near real-time interpretation of absorption effects in elemental distribution images acquired by X-ray fluorescence imaging
    ( 2016)
    Alfeld, M.
    ;
    Wahabzada, Mirwaes
    ;
    Bauckhage, Christian
    ;
    Kersting, Kristian
    ;
    Wellenreuther, G.
    ;
    Barriobero-Vila, P.
    ;
    Requena, G.
    ;
    Boesenberg, U.
    ;
    Falkenberg, G.
    Elemental distribution images acquired by imaging X-ray fluorescence analysis can contain high degrees of redundancy and weakly discernible correlations. In this article near real-time non-negative matrix factorization (NMF) is described for the analysis of a number of data sets acquired from samples of a bi-modal alpha + beta Ti-6Al-6V-2Sn alloy. NMF was used for the first time to reveal absorption artefacts in the elemental distribution images of the samples, where two phases of the alloy, namely alpha and beta, were in superposition. The findings and interpretation of the NMF results were confirmed by Monte Carlo simulation of the layered alloy system. Furthermore, it is shown how the simultaneous factorization of several stacks of elemental distribution images provides uniform basis vectors and consequently simplifies the interpretation of the representation.
  • Publication
    Collective attention on the web
    (Now, 2016)
    Bauckhage, Christian
    ;
    Kersting, Kristian
    Understanding the dynamics of collective human attention has been called a key scientific challenge for the information age. Tackling this challenge, this monograph explores the dynamics of collective attention related to Internet phenomena such as Internet memes, viral videos, or social media platforms and Web-based businesses. To this end, we analyze time series data that directly or indirectly represent how the interest of large populations of Web users in content or services develops over time. Regardless of regional or cultural contexts, we generally observe strong regularities in time series that reflect attention dynamics and we discuss mathematical models that provide plausible explanations as to what drives the apparently dominant dynamics of rapid initial growth and prolonged decline.
  • Publication
    Plant phenotyping using probabilistic topic models: Uncovering the hyperspectral language of plants
    ( 2016)
    Wahabzada, Mirwaes
    ;
    Mahlein, A.-K.
    ;
    Bauckhage, Christian
    ;
    Steiner, U.
    ;
    Oerke, E.-C.
    ;
    Kersting, Kristian
    Modern phenotyping and plant disease detection methods, based on optical sensors and information technology, provide promising approaches to plant research and precision farming. In particular, hyperspectral imaging have been found to reveal physiological and structural characteristics in plants and to allow for tracking physiological dynamics due to environmental effects. In this work, we present an approach to plant phenotyping that integrates non-invasive sensors, computer vision, as well as data mining techniques and allows for monitoring how plants respond to stress. To uncover latent hyperspectral characteristics of diseased plants reliably and in an easy-to-understand way, we "wordify" the hyperspectral images, i.e., we turn the images into a corpus of text documents. Then, we apply probabilistic topic models, a well-established natural language processing technique that identifies content and topics of documents. Based on recent regularized topic models, we demonstrate that one can track automatically the development of three foliar diseases of barley. We also present a visualization of the topics that provides plant scientists an intuitive tool for hyperspectral imaging. In short, our analysis and visualization of characteristic topics found during symptom development and disease progress reveal the hyperspectral language of plant diseases.