Riesen, Kaspar

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Riesen
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Kaspar
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Riesen, Kaspar

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2009 - 200942010 - 202017
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Typ: 01A - Beitrag in wissenschaftlicher Zeitschrift ×

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  • Vorschaubild nicht verfügbar
    Publikation
    Filters for graph-based keyword spotting in historical handwritten documents
    (Elsevier, 2020) Stauffer, Michael; Fischer, Andreas; Riesen, Kaspar [in: Pattern Recognition Letters]
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Approximate graph edit distance in quadratic time
    (IEEE, 2020) Riesen, Kaspar; Ferrer, Miquel; Bunke, Horst [in: IEEE/ACM Transactions on Computational Biology and Bioinformatics]
    Graph edit distance is one of the most flexible and general graph matching models available. The major drawback of graph edit distance, however, is its computational complexity that restricts its applicability to graphs of rather small size. Recently, the authors of the present paper introduced a general approximation framework for the graph edit distance problem. The basic idea of this specific algorithm is to first compute an optimal assignment of independent local graph structures (including substitutions, deletions, and insertions of nodes and edges). This optimal assignment is complete and consistent with respect to the involved nodes of both graphs and can thus be used to instantly derive an admissible (yet suboptimal) solution for the original graph edit distance problem in Ο(n³) time. For large scale graphs or graph sets, however, the cubic time complexity may still be too high. Therefore, we propose to use suboptimal algorithms with quadratic rather than cubic time for solving the basic assignment problem. In particular, the present paper introduces five different greedy assignment algorithms in the context of graph edit distance approximation. In an experimental evaluation, we show that these methods have great potential for further speeding up the computation of graph edit distance while the approximated distances remain sufficiently accurate for graph based pattern classification.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    On the impact of using utilities rather than costs for graph matching
    (Springer, 09.11.2019) Riesen, Kaspar; Bunke, Horst; Fischer, Andreas [in: Neural Processing Letters]
    The concept of graph edit distance constitutes one of the most flexible graph matching paradigms available. The major drawback of graph edit distance, viz. the exponential time complexity, has been recently overcome by means of a reformulation of the edit distance problem to a linear sum assignment problem. However, the substantial speed up of the matching is also accompanied by an approximation error on the distances. Major contribution of this paper is the introduction of a transformation process in order to convert the underlying cost model into a utility model. The benefit of this transformation is that it enables the integration of additional information in the assignment process.We empirically confirm the positive effects of this transformation on five benchmark graph sets with respect to the accuracy and run time of a distance based classifier.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Graph-based keyword spotting in historical manuscripts using Hausdorff edit distance
    (Elsevier, 2019) Ameri, Mohammad Reza; Stauffer, Michael; Riesen, Kaspar; Bui, Tien Dai; Fischer, Andreas; Fischer, Andreas [in: Pattern Recognition Letters]
    Keyword spotting enables content-based retrieval of scanned historical manuscripts using search terms, which, in turn, facilitates the indexation in digital libraries. Recent approaches include graph-based representations that capture the complex structure of handwriting. However, the high representational power of graphs comes at the cost of high computational complexity for graph matching. In this article, we investigate the potential of Hausdorff edit distance (HED) for keyword spotting. It is an efficient quadratic-time approximation of the graph edit distance. In a comprehensive experimental evaluation with four types of handwriting graphs and four benchmark datasets (George Washington, Parzival, Botany, and Alvermann Konzilsprotokolle), we demonstrate a strong performance of the proposed HED-based method when compared with the state of the art, both, in terms of precision and speed.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Online signature verification based on string edit distance
    (Springer, 2019) Riesen, Kaspar; Schmidt, Roman [in: International Journal on Document Analysis and Recognition]
    Handwritten signatures are widely used and well-accepted biometrics for personal authentication. The accuracy of signature verification systems has significantly improved in the last decade, making it possible to rely on machines in particular cases or to support human experts. Yet, based on only few genuine references, signature verification is still a challenging task. The present paper provides a comprehensive comparison of two prominent string matching algorithms that can be readily used for signature verification. Moreover, it evaluates a recent cost model for string matching which turns out to be particularly well suited for the task of signature verification. On three benchmarking data sets, we show that this model outperforms the two standard models for string matching with statistical significance.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Combining graph edit distance and triplet networks for offline signature verification
    (Elsevier, 2019) Maergner, Paul; Pondenkandath, Vinaychandran; Alberti, Michele; Liwicki, Marcus; Riesen, Kaspar; Ingold, Rolf; Fischer, Andreas [in: Pattern Recognition Letters]
    Offline signature verification is a challenging pattern recognition task where a writer model is inferred using only a small number of genuine signatures. A combination of complementary writer models can make it more difficult for an attacker to deceive the verification system. In this work, we propose to combine a recent structural approach based on graph edit distance with a statistical approach based on deep triplet networks. The combination of the structural and statistical models achieve significant improvements in performance on four publicly available benchmark datasets, highlighting their complementary perspectives.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Sketch-based user authentication with a novel string edit distance model
    (IEEE, 2018) Riesen, Kaspar; Hanne, Thomas; Schmidt, Roman [in: IEEE Transactions on Systems, Man, and Cybernetics: Systems]
    The vast majority of user authentication in digital applications is based on alphanumeric passwords. Yet, due to severe problems that might arise with this approach, various efforts have been made in the last decade to replace this authentication paradigm. One candidate for the prospective paradigm shift might be found in the field of graphical passwords. The present paper introduces a novel framework for user authentication based on freehand sketches. The basic idea is that during the registration phase a user draws an arbitrary sketch in a specific drawing canvas (rather than typing a password). Registered users can then be authenticated whenever they are able to reproduce their personal sketch with sufficient precision. The major challenge of such a system is twofold. First, it has to provide a certain degree of error-tolerance such that the authentication of genuine users can be smoothly accomplished. Second, the system should detect even subtle forgeries and reject possible intruders.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    A comparative study of pattern recognition algorithms for predicting the inpatient mortality risk using routine laboratory measurements
    (Springer, 2018) Schütz, Narayan; Leichtle, Alexander Benedikt; Riesen, Kaspar [in: Artificial Intelligence Review]
    Laboratory tests are a common and relatively cheap way to assess the general health status of patients. Various publications showed the potential of laboratory measurements for predicting inpatient mortality using statistical methodologies. However, these efforts are basically limited to the use of logistic regression models. In the present paper we use anonymized data from about 40,000 inpatient admissions to the Inselspital in Bern (Switzerland) to evaluate the potential of powerful pattern recognition algorithms employed for this particular risk prediction. In addition to the age and sex of the inpatients, a set of 33 laboratory measurements, frequently available at the Inselspital, are used as basic variables. In a large empirical evaluation we demonstrate that recent pattern recognition algorithms (such as random forests, gradient boosted trees or neural networks) outperform the more traditional approaches based on logistic regression. Moreover, we show how the predictions of the pattern recognition algorithms, which cannot be directly interpreted in general, can be calibrated to output a meaningful probabilistic risk score.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Keyword spotting in historical handwritten documents based on graph matching
    (Elsevier, 2018) Stauffer, Michael; Fischer, Andreas; Riesen, Kaspar [in: Pattern Recognition]
    In the last decades historical handwritten documents have become increasingly available in digital form. Yet, the accessibility to these documents with respect to browsing and searching remained limited as full automatic transcription is often not possible or not sufficiently accurate. This paper proposes a novel reliable approach for template-based keyword spotting in historical handwritten documents. In particular, our framework makes use of different graph representations for segmented word images and a sophisticated matching procedure. Moreover, we extend our method to a spotting ensemble. In an exhaustive experimental evaluation on four widely used benchmark datasets we show that the proposed approach is able to keep up or even outperform several state-of-the-art methods for template- and learning-based keyword spotting.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild nicht verfügbar
    Publikation
    01A - Beitrag in wissenschaftlicher Zeitschrift