Riesen, Kaspar

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Riesen
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Kaspar
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Riesen, Kaspar

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2008 - 200912010 - 202021
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Autor:in: Fischer, Andreas ×

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Gerade angezeigt 1 - 10 von 22
  • 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
    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
    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
    Graph embedding for offline handwritten signature verification
    (2019) Stauffer, Michael; Maergner, Paul; Fischer, Andreas; Riesen, Kaspar [in: ICBEA 2019. Proceedings of 2019 3rd International Conference on Biometric Engineering and Applications (ICBEA 2019). Stockholm, Sweden, May 29-31, 2019]
    Due to the high availability and applicability, handwritten signatures are an eminent biometric authentication measure in our life. To mitigate the risk of a potential misuse, automatic signature verification tries to distinguish between genuine and forged signatures. Most of the available signature verification approaches make use of vectorial rather than graph-based representations of the handwriting. This is rather surprising as graphs offer some inherent advantages. Graphs are, for instance, able to directly adapt their size and structure to the size and complexity of the respective handwritten entities. Moreover, several fast graph matching algorithms have been proposed recently that allow to employ graphs also in domains with large amounts of data. The present paper proposes to use different graph embedding approaches in conjunction with a recent graph-based signature verification framework. That is, signature graphs are not directly matched with each other, but first compared with a set of predefined prototype graphs, in order to obtain a dissimilarity representation. In an experimental evaluation, we employ the proposed method on two widely used benchmark datasets. On both datasets, we empirically confirm that the learning-free graph embedding outperforms state-of-the-art methods with respect to both accuracy and runtime.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Cross-evaluation of graph-based keyword spotting in handwritten historical documents
    (Springer, 2019) Stauffer, Michael; Maergner, Paul; Fischer, Andreas; Riesen, Kaspar; Conte, Donatello; Ramel, Jean-Yves; Foggia, Pasquale [in: Graph-Based Representations in Pattern Recognition. 12th IAPR-TC-15 International Workshop, GbRPR 2019, Tours, France, June 19-21, 2019. Proceedings]
    In contrast to statistical representations, graphs offer some inherent advantages when it comes to handwriting representation. That is, graphs are able to adapt their size and structure to the individual handwriting and represent binary relationships that might exist within the handwriting. We observe an increasing number of graph-based keyword spotting frameworks in the last years. In general, keyword spotting allows to retrieve instances of an arbitrary query in documents. It is common practice to optimise keyword spotting frameworks for each document individually, and thus, the overall generalisability remains somehow questionable. In this paper, we focus on this question by conducting a cross-evaluation experiment on four handwritten historical documents. We observe a direct relationship between parameter settings and the actual handwriting. We also propose different ensemble strategies that allow to keep up with individually optimised systems without a priori knowledge of a certain manuscript. Such a system can potentially be applied to new documents without prior optimisation.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Offline signature verification using structural dynamic time warping
    (IEEE, 2019) Stauffer, Michael; Maergner, Paul; Fischer, Andreas; Ingold, Rolf; Riesen, Kaspar [in: ICDAR 2019. The 15th IAPR International Conference on Document Analysis and Recognition. 20-25 September 2019, Sydney, Australia. Proceedings]
    In recent years, different approaches for handwriting recognition that are based on graph representations have been proposed (e.g. graph-based keyword spotting or signature verification). This trend is mostly due to the availability of novel fast graph matching algorithms, as well as the inherent flexibility and expressivity of graph data structures when compared to vectorial representations. That is, graphs are able to directly adapt their size and structure to the size and complexity of the respective handwritten entities. However, the vast majority of the proposed approaches match the graphs from a global perspective only. In the present paper, we propose to match the underlying graphs from different local perspectives and combine the resulting assignments by means of Dynamic Time Warping. Moreover, we show that the proposed approach can be readily combined with global matchings. In an experimental evaluation, we employ the novel method in a signature verification scenario on two widely used benchmark datasets. On both datasets, we empirically confirm that the proposed approach outperforms state-of-the-art methods with respect to both accuracy and runtime.
    04B - Beitrag Konferenzschrift
  • 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
    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
    Offline signature verification by combining graph edit distance and triplet networks
    (Springer, 2018) Maergner, Paul; Pondenkandath, Vinaychandran; Alberti, Michele; Liwicki, Marcus; Riesen, Kaspar; Ingold, Rolf; Fischer, Andreas; Bai, Xiao; Hancock, Edwin R.; Ho, Tin Kam; Wilson, Richard C.; Biggio, Battista; Robles-Kelly, Antonio [in: Structural, syntactic, and statistical pattern recognition. Joint IAPR International Workshop, S+SSPR 2018, Beijing, China, August 17-19, 2018. Proceedings]
    Biometric authentication by means of handwritten signatures is a challenging pattern recognition task, which aims to infer a writer model from only a handful of genuine signatures. In order to make it more difficult for a forger to attack the verification system, a promising strategy is to combine different writer models. In this work, we propose to complement a recent structural approach to offline signature verification based on graph edit distance with a statistical approach based on metric learning with deep neural networks. On the MCYT and GPDS benchmark datasets, we demonstrate that combining the structural and statistical models leads to significant improvements in performance, profiting from their complementary properties.
    04B - Beitrag Konferenzschrift
  • Vorschaubild nicht verfügbar
    Publikation
    Offline signature verification via structural methods: graph edit distance and inkball models
    (IEEE, 2018) Maergner, Paul; Howe, Nicholas; Riesen, Kaspar; Ingold, Rolf; Fischer, Andreas [in: ICFHR2018. 2018 16th International Conference on Frontiers in Handwriting Recognition. Niagara Falls, New York, USA, 5-8 August 2018. Proceedings]
    For handwritten signature verification, signature images are typically represented with fixed-sized feature vectors capturing local and global properties of the handwriting. Graphbased representations offer a promising alternative, as they are flexible in size and model the global structure of the handwriting. However, they are only rarely used for signature verification, which may be due to the high computational complexity involved when matching two graphs. In this paper, we take a closer look at two recently presented structural methods for handwriting analysis, for which efficient matching methods are available: keypoint graphs with approximate graph edit distance and inkball models. Inkball models, in particular, have never been used for signature verification before. We investigate both approaches individually and propose a combined verification system, which demonstrates an excellent performance on the MCYT and GPDS benchmark data sets when compared with the state of the art.
    04B - Beitrag Konferenzschrift