Luka Fürst

Selecting features for object detection using an AdaBoost-compatible evaluation function

This paper addresses the problem of selecting features in a visual object detection setup where a detection algorithm is applied to an input image represented by a set of features. The set of features to be employed in the test stage is prepared in two training-stage steps. In the first step, a feature extraction algorithm produces a (possibly large) initial set of features. In the second step, on which this paper focuses, the initial set is reduced using a selection procedure. The proposed selection procedure is based on a novel evaluation function that measures the utility of individual features for a certain detection task. Owing to its design, the evaluation function can be seamlessly embedded into an AdaBoost selection framework. The developed selection procedure is integrated with state-of-the-art feature extraction and object detection methods. The presented system was tested on five challenging detection setups. In three of them, a fairly high detection accuracy was effected by as few as six features selected out of several hundred initial candidates.

Converting metamodels to graph grammars: doing without advanced graph grammar features

In this paper, we present a method to convert a metamodel in the form of a UML class diagram into a context-sensitive graph grammar whose language comprises precisely the set of model graphs (UML object diagrams) that conform to the input metamodel. Compared to other approaches that deal with the same problem, we use a graph grammar formalism that does not employ any advanced graph grammar features, such as application conditions, precedence rules, and production schemes. Specifically, we use Rekers and Schürr’s Layered Graph Grammars, which may be regarded as a pure generalization of standard context-sensitive string grammars. We show that elementary grammatical features, i.e., grammar labels and context-sensitive graph rewrite rules, suffice to represent metamodels with arbitrary multiplicities and inheritance. Inspired by attribute string grammars, we also propose a graph-grammar-based approach to the semantic analysis of model graphs.

Introduction of the automated assessment of homework assignments in a university-level programming course

Modern teaching paradigms promote active student participation, encouraging teachers to adapt the teaching process to involve more practical work. In the introductory programming course at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia, homework assignments contribute approximately one half to the total grade, requiring a significant investment of time and human resources in the assessment process. This problem was alleviated by the automated assessment of homework assignments. In this paper, we introduce an automated assessment system for programming assignments that includes dynamic testing of student programs, plagiarism detection, and a proper presentation of the results. We share our experience and compare the introduced system with the manual assessment approach used before.

Improving the graph grammar parser of Rekers and Schurr

Graph grammars and graph grammar parsers are to visual languages what string grammars and parsers are to textual languages. A graph grammar specifies a set of valid graphs and can thus be used to formalise the syntax of a visual language. A graph grammar parser is a tool for recognising valid programs in such a formally defined visual language. A parser for context-sensitive graph grammars, which have proved to be suitable for formalising real-world visual languages, was developed by Rekers and Schurr. We propose three improvements of this parser. One of them enlarges the class of parsable graph grammars, while the other two increase the parsers computational efficiency. Experimental results show that for some (meaningful) graph grammars, our improvements can enhance the parsers performance by orders of magnitude. The proposed improvements will hopefully increase both the parsers applicability and the interest in visual language parsing in general.

Exploratory equivalence in graphs: Definition and algorithms

Motivated by improving the efficiency of pattern matching on graphs, we define a new kind of equivalence on graph vertices. Since it can be used in various graph algorithms that explore graphs, we call it exploratory equivalence. The equivalence is based on graph automorphisms. Because many similar equivalences exist (some also based on automorphisms), we argue that this one is novel. For each graph, there are many possible exploratory equivalences, but for improving the efficiency of the exploration, some are better than others. To this end, we define a goal function that models the reduction of the search space in such algorithms. We describe two greedy algorithms for the underlying optimization problem. One is based directly on the definition using a straightforward greedy criterion, whereas the second one uses several practical speedups and a different greedy criterion. Finally, we demonstrate the huge impact of exploratory equivalence on a real application, i.e., graph grammar parsing.

Maximum exploratory equivalence in trees

Many practical problems are modeled with networks and graphs. Their exploration is of significant importance, and several graph-exploration algorithms already exist. In this paper, we focus on a type of vertex equivalence, called exploratory equivalence, which has a great potential to speed up such algorithms. It is an equivalence based on graph automorphisms and can, for example, help us in solving the subgraph isomorphism problem, which is a well-known NP-hard problem. In particular, if a given pattern graph has nontrivial automorphisms, then each of its nontrivial exploratory equivalent classes gives rise to a set of constraints to prune the search space of solutions. In the paper, we define the maximum exploratory equivalence problem. We show that the defined problem is at least as hard the graph isomorphism problem. Additionally, we present a polynomial-time algorithm for solving the problem when the input is restricted to tree graphs. Furthermore, we show that for trees, a maximum exploratory equivalent partition leads to a globally optimal set of subgraph isomorphism constraints, whereas this is not necessarily the case for general graphs.