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Dariusz Małyszko

Bio: Dariusz Małyszko is an academic researcher from Bialystok University of Technology. The author has contributed to research in topics: Cluster analysis & Entropy (information theory). The author has an hindex of 5, co-authored 17 publications receiving 132 citations.

Papers
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Journal ArticleDOI
TL;DR: Performance in experimental assessment suggests that granular multilevel rough entropy threshold based segmentation – MRET – present high quality, comparable with and often better than k-means clustering based segmentations.

61 citations

Book ChapterDOI
23 Oct 2008
TL;DR: New algorithmic schemes Standard RECA and Fuzzy RECA in the area of rough entropy based partitioning routines have been proposed, taking advantage of dealing with some degree of uncertainty in analyzed data.
Abstract: Clustering or data grouping presents fundamental initial procedure in image processing. This paper addresses the problem of combining the concept of rough sets and entropy measure in the area of image segmentation. In the present study, comprehensive investigation into rough set entropy based thresholding image segmentation techniques has been performed. Segmentation presents the low-level image transformation routine concerned with image partitioning into distinct disjoint and homogenous regions with thresholding algorithms most often applied in practical solutions when there is pressing need for simplicity and robustness. Simultaneous combining entropy based thresholding with rough sets results in rough entropy thresholding algorithm. In the present paper, new algorithmic schemes Standard RECA(Rough Entropy Clustering Algorithm) and Fuzzy RECAin the area of rough entropy based partitioning routines have been proposed. Rough entropy clustering incorporates the notion of rough entropy into clustering model taking advantage of dealing with some degree of uncertainty in analyzed data. Both Standard and Fuzzy RECAalgorithmic schemes performed usually equally robustly compared to standard k-means algorithm. At the same time, in many runs yielding slightly better performance making possible future implementation in clustering applications.

24 citations

Journal ArticleDOI
TL;DR: In order to prove the relevance of the proposed rough entropy measures, the evaluation of rough entropy segmentations based on the comparison with human segmentations from Berkeley and Weizmann image databases has been presented and seems to comprehend properly properties validated by different image segmentation quality indices.
Abstract: High quality performance of image segmentation methods presents one leading priority in design and implementation of image analysis systems. Incorporating the most important image data information into segmentation process has resulted in development of innovative frameworks such as fuzzy systems, rough systems and recently rough - fuzzy systems. Data analysis based on rough and fuzzy systems is designed to apprehend internal data structure in case of incomplete or uncertain information. Rough entropy framework proposed in [12,13] has been dedicated for application in clustering systems, especially for image segmentation systems. We extend that framework into eight distinct rough entropy measures and related clustering algorithms. The introduced solutions are capable of adaptive incorporation of the most important factors that contribute to the relation between data objects and makes possible better understanding of the image structure. In order to prove the relevance of the proposed rough entropy measures, the evaluation of rough entropy segmentations based on the comparison with human segmentations from Berkeley and Weizmann image databases has been presented. At the same time, rough entropy based measures applied in the domain of image segmentation quality evaluation have been compared with standard image segmentation indices. Additionally, rough entropy measures seem to comprehend properly properties validated by different image segmentation quality indices.

17 citations

01 Jan 2008
TL;DR: Experimental results suggest that proposed solution of image segmentation in 2D domain by means of maximizing rough entropy measure in granular computing setting has proved to be equally robust as standard Otsu method.
Abstract: The paper addresses the problem of image segmentation in 2D domain by means of maximizing rough entropy measure in granular computing setting. Proposed 2D multilevel rough entropy thresholding extends multilevel thresholding scheme into 2D image thresholding. Proposed thresholding algorithm 2D MRET has been compared with standard multilevel thresholding based on Otsu method. Experimental results suggest that proposed solution has proved to be equally robust as standard Otsu method, outperforming the latter in many experimental runs.

15 citations

Book ChapterDOI
15 Dec 2009
TL;DR: Experimental results suggest that proposed algorithm outperforms standard k-means clustering methods applied in the area of image segmentation.
Abstract: Data clustering algorithmic schemes receive much careful research insight due to the prominent role that clustering plays during data analysis. Proper data clustering reveals data structure and makes possible further data processing and analysis. In the application area, k-means clustering algorithms are most often exploited in almost all important branches of data processing and data exploration. During last decades, a great deal of new algorithmic techniques have been invented and implemented that extend basic k-means clustering methods. In this context, fuzzy and rough k-means clustering presents robust modifications of basic k-means clustering that are aimed at better apprehension of data structure that advantageously incorporate notions from fuzzy and rough set theories. In the paper, an extension of rough k-means clustering into rough entropy domain has been introduced. Experimental results suggest that proposed algorithm outperforms standard k-means clustering methods applied in the area of image segmentation.

10 citations


Cited by
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Journal ArticleDOI
TL;DR: This article compares k-mean to fuzzy c-means and rough k-Means as important representatives of soft clustering, and surveys important extensions and derivatives of these algorithms.

157 citations

Journal ArticleDOI
TL;DR: The summarization and extension of the results obtained since 2003 when investigations on foundations of approximation of partially defined concepts are presented, including examples of rough set-based strategies for the extension of approximation spaces from samples of objects onto a whole universe of objects.

138 citations

Journal ArticleDOI
01 Oct 2014
TL;DR: The comparison of evolutionary and swarm-based optimization algorithms for multilevel color image thresholding problem, which is a process used for segmentation of an image into different regions, exposed that evolutionary algorithms are faster than swarm based algorithms in terms of CPU running times.
Abstract: This paper introduces the comparison of evolutionary and swarm-based optimization algorithms for multilevel color image thresholding problem which is a process used for segmentation of an image into different regions. Thresholding has various applications such as video image compression, geovideo and document processing, particle counting, and object recognition. Evolutionary and swarm-based computation techniques are widely used to reduce the computational complexity of the multilevel thresholding problem. In this study, well-known evolutionary algorithms such as Evolution Strategy, Genetic Algorithm, Differential Evolution, Adaptive Differential Evolution and swarm-based algorithms such as Particle Swarm Optimization, Artificial Bee Colony, Cuckoo Search and Differential Search Algorithm have been used for solving multilevel thresholding problem. Kapur's entropy is used as the fitness function to be maximized. Experiments are conducted on 20 different test images to compare the algorithms in terms of quality, running CPU times and compression ratios. According to the statistical analysis of objective values, swarm based algorithms are more accurate and robust than evolutionary algorithms in general. However, experimental results exposed that evolutionary algorithms are faster than swarm based algorithms in terms of CPU running times.

119 citations

Journal ArticleDOI
TL;DR: This paper aims at introducing the concept of cross-entropy for uncertain variables based on uncertain theory, as well as investigating some mathematical properties of this concept.

102 citations

Journal ArticleDOI
TL;DR: Inspired by three-way decisions, this paper proposes Type-1 variable precision multigranulation decision-theoretic fuzzy rough sets, which are based on single granulation rough sets and two operators based on this membership degree.
Abstract: This paper studies variable precision multigranulation fuzzy decision-theoretic rough sets in an information system. We firstly review definitions and properties of multigranulation fuzzy rough sets. A novel membership degree based on single granulation rough sets is proposed. Then two operators based on this membership degree are defined. By employing these operators, two types of variable precision multigranulation fuzzy rough sets in an information system are proposed. Finally, inspired by three-way decisions, we propose Type-1 variable precision multigranulation decision-theoretic fuzzy rough sets.

70 citations