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Open access

Miroslav Hudec, Erika Bednárová and Andreas Holzinger

Abstract

Data from National Statistical Institutes is generally considered an important source of credible evidence for a variety of users. Summarization and dissemination via traditional methods is a convenient approach for providing this evidence. However, this is usually comprehensible only for users with a considerable level of statistical literacy. A promising alternative lies in augmenting the summarization linguistically. Less statistically literate users (e.g., domain experts and the general public), as well as disabled people can benefit from such a summarization. This article studies the potential of summaries expressed in short quantified sentences. Summaries including, for example, “most visits from remote countries are of a short duration” can be immediately understood by diverse users. Linguistic summaries are not intended to replace existing dissemination approaches, but can augment them by providing alternatives for the benefit of diverse users of official statistics. Linguistic summarization can be achieved via mathematical formalization of linguistic terms and relative quantifiers by fuzzy sets. To avoid summaries based on outliers or data with low coverage, a quality criterion is applied. The concept based on linguistic summaries is demonstrated on test interfaces, interpreting summaries from real municipal statistical data. The article identifies a number of further research opportunities, and demonstrates ways to explore those.

Open access

Peter Lukáč, Róbert Hudec, Miroslav Benčo, Zuzana Dubcová, Martina Zachariášová and Patrik Kamencay

The Evaluation Criterion for Color Image Segmentation Algorithms

Image segmentation is first and very important step in image analysis. The main idea of image segmentation is to simplify and change image into easier and meaningful form to analyze. Image segmentation is process, which locate objects in image. Many segmentation algorithms have been created for different applications. The algorithms are used in traffic applications, army applications, web applications, medical applications, studying and many others. In present time, do not exist restful objective methods to evaluate segmentation algorithms. This paper presents evaluation criterion based on measurement of precision of boundary segmentation. Moreover, the automatic segmentation algorithms in comparison with human segmentation results were tested. Four most used image segmentation algorithms, namely, Efficient graph based, K-means, Mean shift and Belief propagation are compared by designed criterion. The criterion computes three evaluation parameters like precision, recall and F 1 and the results are presented in the tables and graphs at the end of the paper.