Michel Neuhaus

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Spend your few moment to read a book even only few pages. Reading book is not obligation and force for everybody. When you don't want to read, you can get punishment from the publisher. Read a book becomes a choice of your different characteristics. Many people with reading habit will always be enjoyable to read, or on the contrary. For some reasons, this(More)
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,(More)
A common approach in structural pattern classification is to define a dissimilarity measure on patterns and apply a distance-based nearest-neighbor classifier. In this paper, we introduce an alternative method for classification using kernel functions based on edit distance. The proposed approach is applicable to both string and graph representations of(More)
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.(More)
Graph edit distance is a powerful error-tolerant similarity measure for graphs. For pattern recognition problems involving large graphs, however, the high computational complexity makes it sometimes impossible to apply edit distance algorithms. In the present paper we propose an efficient algorithm for edit distance computation of planar graphs. Given(More)
General graph matching methods often suffer from the lack of mathematical structure in the space of graphs. Using kernel functions to evaluate structural graph similarity allows us to formulate the graph matching problem in an implicitly existing vector space and to apply well-known methods for pattern analysis. In this paper we propose a novel convolution(More)
One of the major difficulties in graph classification is the lack of mathematical structure in the space of graphs. The use of kernel machines allows us to overcome this fundamental limitation in an elegant manner by addressing the pattern recognition problem in an implicitly existing feature vector space instead of the original space of graphs. In this(More)
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(More)