Retrieving graphs containing a query graph from a large graph database is a key task in many graph-based applications, including chemical compounds discovery, protein complex prediction, and structural pattern recognition. However, graph data handled by these applications is often noisy, incomplete, and inaccurate because of the way the data is produced. In this paper,we study subgraph queries ...

This work discusses a method for extracting quantitative measures from terminology and instance base components of semantic models. The method introduces a multi-criteria analysis while comparing ontology models and assessing semantic similarity. The article presents theoretical overview of the method and tool implementing evaluation schemes supplemented with practical examples. The capabilitie...

Vector quantization(VQ) is a lossy data compression technique from signal processing, which is restricted to feature vectors and therefore inapplicable for combinatorial structures. This contribution aims at extending VQ to the quantization of graphs in a theoretically principled way in order to overcome practical limitations known in the context of prototype-based clustering of graphs. For thi...

We are proposing an approach for applying design patterns that consists of recognizing occurrences of the modeling problem solved by the design pattern (problem pattern) in input models, which are then transformed according to the solution proposed by the design pattern (solution pattern). In this paper, we look at the issue of identifying instances of problem patterns in input models, and mark...

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-dimensio...

The definition of efficient similarity or dissimilarity measures between graphs is a key problem in structural pattern recognition. This problem is nicely addressed by the graph edit distance, which constitutes one of the most flexible graph dissimilarity measure in this field. Unfortunately, the computation of an exact graph edit distance is known to be exponential in the number of nodes. In t...

Syntactic methods in pattern recognition have been around for more than 20 years now [22, 23], and numerous applications have been done with these basic techniques. Structural pattern recognition is an even vaster field [47] (syntactic methods can actually be considered to belong to it), where many methods exist and have been used for various practical applications. These methods have been exte...

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 inter...

Modern airborne synthetic aperture radar sensors provide high spatial resolution data. Experimental systems have even achieved decimetre resolution. In such data, many features of urban objects can be identified, which are beyond what has been achieved by radar remote sensing before. An example for the new quality of the appearance of urban man-made objects such as buildings in these data is gi...

Finite structures such as point patterns, strings, trees, and graphs occur as ”natural” representations of structured data in different application areas of machine learning. We develop the theory of structure spaces and derive geometrical and analytical concepts such as the angle between structures and the derivative of functions on structures. In particular, we show that the gradient of a dif...