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40 results on '"Neuhaus, Michel"'

1. Circular Economy in der Region voranbringen

Author

Neuhaus, Michel, Beyer, Joachim, Mehl, Fabian, Hermandi, Carina, Rosenbaum, Janne, Beier, Silvio, Series Editor, Hense, Peter, Series Editor, Klümper, Claudia, Series Editor, Lechtenböhmer, Stefan, Series Editor, Reicher, Christa, Series Editor, Büttner, Sabine, editor, Handmann, Uwe, editor, and Irrek, Wolfgang, editor

Published
2024
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2. Das CSR-Kompetenzzentrum Ruhr

Author

Degenhardt, Julia-Marie, Elias, Arne, Kromminga, Peter, Lachmann, Detlev, Neuhaus, Michel, Zamor, Caroline, Schmidpeter, Prof. Dr. René, Series Editor, and Schmitz, Marina, editor

Published
2021
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4. Graph Embedding in Vector Spaces by Means of Prototype Selection

Author

Riesen, Kaspar, Neuhaus, Michel, Bunke, Horst, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Escolano, Francisco, editor, and Vento, Mario, editor

Published
2007
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5. A Quadratic Programming Approach to the Graph Edit Distance Problem

Author

Neuhaus, Michel, Bunke, Horst, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Escolano, Francisco, editor, and Vento, Mario, editor

Published
2007
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6. Bipartite Graph Matching for Computing the Edit Distance of Graphs

Author

Riesen, Kaspar, Neuhaus, Michel, Bunke, Horst, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Escolano, Francisco, editor, and Vento, Mario, editor

Published
2007
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7. Multiple Classifier Systems for Embedded String Patterns

Author

Spillmann, Barbara, Neuhaus, Michel, Bunke, Horst, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Schwenker, Friedhelm, editor, and Marinai, Simone, editor

Published
2006
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8. Transforming Strings to Vector Spaces Using Prototype Selection

Author

Spillmann, Barbara, Neuhaus, Michel, Bunke, Horst, Pękalska, Elżbieta, Duin, Robert P. W., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yeung, Dit-Yan, editor, Kwok, James T., editor, Fred, Ana, editor, Roli, Fabio, editor, and de Ridder, Dick, editor

Published
2006
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9. A Random Walk Kernel Derived from Graph Edit Distance

Author

Neuhaus, Michel, Bunke, Horst, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yeung, Dit-Yan, editor, Kwok, James T., editor, Fred, Ana, editor, Roli, Fabio, editor, and de Ridder, Dick, editor

Published
2006
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10. Fast Suboptimal Algorithms for the Computation of Graph Edit Distance

Author

Neuhaus, Michel, Riesen, Kaspar, Bunke, Horst, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yeung, Dit-Yan, editor, Kwok, James T., editor, Fred, Ana, editor, Roli, Fabio, editor, and de Ridder, Dick, editor

Published
2006
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11. Edit Distance Based Kernel Functions for Attributed Graph Matching

Author

Neuhaus, Michel, Bunke, Horst, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Brun, Luc, editor, and Vento, Mario, editor

Published
2005
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12. Graph-Based Multiple Classifier Systems A Data Level Fusion Approach

Author

Neuhaus, Michel, Bunke, Horst, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Roli, Fabio, editor, and Vitulano, Sergio, editor

Published
2005
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13. Graph Matching – Challenges and Potential Solutions

Author

Bunke, Horst, Irniger, Christophe, Neuhaus, Michel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Roli, Fabio, editor, and Vitulano, Sergio, editor

Published
2005
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14. A Graph Matching Based Approach to Fingerprint Classification Using Directional Variance

Author

Neuhaus, Michel, Bunke, Horst, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Jain, Anil, editor, and Ratha, Nalini K., editor

Published
2005
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15. An Error-Tolerant Approximate Matching Algorithm for Attributed Planar Graphs and Its Application to Fingerprint Classification

Author

Neuhaus, Michel, Bunke, Horst, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Fred, Ana, editor, Caelli, Terry M., editor, Duin, Robert P. W., editor, Campilho, Aurélio C., editor, and de Ridder, Dick, editor

Published
2004
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18. Self-organizing maps for learning the edit costs in graph matching

Author

Neuhaus, Michel and Bunke, Horst

Subjects
Machine learning -- Research
Abstract

Although graph matching and graph edit distance computation have become areas of intensive research recently, the automatic inference of the cost of edit operations has remained an open problem. In the present paper, we address the issue of learning graph edit distance cost functions for numerically labeled graphs from a corpus of sample graphs. We propose a system of self-organizing maps (SOMs) that represent the distance measuring spaces of node and edge labels. Our learning process is based on the concept of self-organization. It adapts the edit costs in such a way that the similarity of graphs from the same class is increased, whereas the similarity of graphs from different classes decreases. The learning procedure is demonstrated on two different applications involving line drawing graphs and graphs representing diatoms, respectively. Index Terms--Cost learning, graph edit distance, graph matching, self-organizing map.

Published
2005

33. A Quadratic Programming Approach to the Graph Edit Distance Problem.

Author

Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Escolano, Francisco, Vento, Mario, Neuhaus, Michel, and Bunke, Horst

Abstract

In this paper we propose a quadratic programming approach to computing the edit distance of graphs. Whereas the standard edit distance is defined with respect to a minimum-cost edit path between graphs, we introduce the notion of fuzzy edit paths between graphs and provide a quadratic programming formulation for the minimization of fuzzy edit costs. Experiments on real-world graph data demonstrate that our proposed method is able to outperform the standard edit distance method in terms of recognition accuracy on two out of three data sets. [ABSTRACT FROM AUTHOR]

Published
2007
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34. Bipartite Graph Matching for Computing the Edit Distance of Graphs.

Author

Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Escolano, Francisco, Vento, Mario, Riesen, Kaspar, Neuhaus, Michel, and Bunke, Horst

Abstract

In the field of structural pattern recognition graphs constitute a very common and powerful way of representing patterns. In contrast to string representations, graphs allow us to describe relational information in the patterns under consideration. One of the main drawbacks of graph representations is that the computation of standard graph similarity measures is exponential in the number of involved nodes. Hence, such computations are feasible for rather small graphs only. One of the most flexible error-tolerant graph similarity measures is based on graph edit distance. In this paper we propose an approach for the efficient compuation of edit distance based on bipartite graph matching by means of Munkres' algorithm, sometimes referred to as the Hungarian algorithm. Our proposed algorithm runs in polynomial time, but provides only suboptimal edit distance results. The reason for its suboptimality is that implied edge operations are not considered during the process of finding the optimal node assignment. In experiments on semi-artificial and real data we demonstrate the speedup of our proposed method over a traditional tree search based algorithm for graph edit distance computation. Also we show that classification accuracy remains nearly unaffected. [ABSTRACT FROM AUTHOR]

Published
2007
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35. Transforming Strings to Vector Spaces Using Prototype Selection.

Author

Yeung, Dit-Yan, Kwok, James T., Fred, Ana, Roli, Fabio, de Ridder, Dick, Spillmann, Barbara, Neuhaus, Michel, Bunke, Horst, Pękalska, Elżbieta, and Duin, Robert P. W.

Abstract

A common way of expressing string similarity in structural pattern recognition is the edit distance. It allows one to apply the kNN rule in order to classify a set of strings. However, compared to the wide range of elaborated classifiers known from statistical pattern recognition, this is only a very basic method. In the present paper we propose a method for transforming strings into n-dimensional real vector spaces based on prototype selection. This allows us to subsequently classify the transformed strings with more sophisticated classifiers, such as support vector machine and other kernel based methods. In a number of experiments, we show that the recognition rate can be significantly improved by means of this procedure. [ABSTRACT FROM AUTHOR]

Published
2006
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36. A Random Walk Kernel Derived from Graph Edit Distance.

Author

Yeung, Dit-Yan, Kwok, James T., Fred, Ana, Roli, Fabio, de Ridder, Dick, Neuhaus, Michel, and Bunke, Horst

Abstract

Random walk kernels in conjunction with Support Vector Machines are powerful methods for error-tolerant graph matching. Because of their local definition, however, the applicability of random walk kernels strongly depends on the characteristics of the underlying graph representation. In this paper, we describe a simple extension to the standard random walk kernel based on graph edit distance. The idea is to include global matching information in the local similarity evaluation of random walks in graphs. The proposed extension allows us to improve the performance of the random walk kernel significantly. We present an experimental evaluation of our method on three difficult graph datasets. [ABSTRACT FROM AUTHOR]

Published
2006
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37. Fast Suboptimal Algorithms for the Computation of Graph Edit Distance.

Author

Yeung, Dit-Yan, Kwok, James T., Fred, Ana, Roli, Fabio, de Ridder, Dick, Neuhaus, Michel, Riesen, Kaspar, and Bunke, Horst

Abstract

Graph edit distance is one of the most flexible mechanisms for error-tolerant graph matching. Its key advantage is that edit distance is applicable to unconstrained attributed graphs and can be tailored to a wide variety of applications by means of specific edit cost functions. Its computational complexity, however, is exponential in the number of vertices, which means that edit distance is feasible for small graphs only. In this paper, we propose two simple, but effective modifications of a standard edit distance algorithm that allow us to suboptimally compute edit distance in a faster way. In experiments on real data, we demonstrate the resulting speedup and show that classification accuracy is mostly not affected. The suboptimality of our methods mainly results in larger inter-class distances, while intra-class distances remain low, which makes the proposed methods very well applicable to distance-based graph classification. [ABSTRACT FROM AUTHOR]

Published
2006
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38. Graph-Based Multiple Classifier Systems A Data Level Fusion Approach.

Author

Roli, Fabio, Vitulano, Sergio, Neuhaus, Michel, and Bunke, Horst

Abstract

The combination of multiple classifiers has been successful in improving classification accuracy in many pattern recognition problems. For graph matching, the fusion of classifiers is normally restricted to the decision level. In this paper we propose a novel fusion method for graph patterns. Our method detects common parts in graphs in an error-tolerant way using graph edit distance and constructs graphs representing the common parts only. In experiments, we demonstrate on two datasets that the method is able to improve the classification of graphs. [ABSTRACT FROM AUTHOR]

Published
2005
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39. Edit Distance Based Kernel Functions for Attributed Graph Matching.

Author

Brun, Luc, Vento, Mario, Neuhaus, Michel, and Bunke, Horst

Abstract

In this paper we propose the use of a simple kernel function based on the graph edit distance. The kernel function allows us to apply a wide range of statistical algorithms to the problem of attributed graph matching. The function we describe is simple to compute and leads to several convenient interpretations of geometric properties of graphs in their implicit vector space representation. Although the function is not generally positive definite, we show in experiments on real-world data that the kernel approach may result in a significant improvement of the graph matching and classification performance using support vector machines and kernel principal component analysis. [ABSTRACT FROM AUTHOR]

Published
2005

40. A Graph Matching Based Approach to Fingerprint Classification Using Directional Variance.

Author

Kanade, Takeo, Jain, Anil, Ratha, Nalini K., Neuhaus, Michel, and Bunke, Horst

Abstract

In the present paper we address the fingerprint classification problem with a structural pattern recognition approach. Our main contribution is the definition of modified directional variance in orientation vector fields. The new directional variance allows us to extract regions from fingerprints that are relevant for the classification in the Henry scheme. After processing the regions of interest, the resulting structures are converted into attributed graphs. The classification is finally performed with an efficient graph edit distance algorithm. The performance of the proposed classification method is evaluated on the NIST-4 database of fingerprints. [ABSTRACT FROM AUTHOR]

Published
2005
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