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1
Content available remote A Similarity Measure for Cyclic Unary Regular Languages
100%
Dassow J. , Martin G. M. , Vico F. J.
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tom Vol. 96, nr 1/2
71-88
EN
A cyclic unary regular language is a regular language over a unary alphabet that is represented by a cyclic automaton. We propose a similarity measure for cyclic unary regular languages by modifying the Jaccard similarity coefficient and the Sorensen coefficient to measure the level of overlap between such languages. This measure computes the proportion of strings that are shared by two or more cyclic unary regular languages and is an upper bound of the Jaccard coefficient and the Sorensen coefficient. By using such similarity measure, we define a dissimilarity measure for cyclic unary regular languages that is a semimetric distance. Moreover, it can be used for the non-cyclic case.
2
Content available remote Tensor Framework and Combined Symmetry for Hypertext Mining
100%
Saha S. , Murthy C. A. , Pal S. K.
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tom Vol. 97, nr 1/2
215-234
EN
We have made a case here for utilizing tensor framework for hypertext mining. Tensor is a generalization of vector and tensor framework discussed here is a generalization of vector space model which is widely used in the information retrieval and web mining literature. Most hypertext documents have an inherent internal tag structure and external link structure that render the desirable use of multidimensional representations such as those offered by tensor objects. We have focused on the advantages of Tensor Space Model, in which documents are represented using sixth-order tensors. We have exploited the local-structure and neighborhood recommendation encapsulated by the proposed representation. We have defined a similarity measure for tensor objects corresponding to hypertext documents, and evaluated the proposed measure for mining tasks. The superior performance of the proposed methodology for clustering and classification tasks of hypertext documents have been demonstrated here. The experiment using different types of similarity measure in the different components of hypertext documents provides the main advantage of the proposed model. It has been shown theoretically that, the computational complexity of an algorithm performing on tensor framework using tensor similarity measure as distance is at most the computational complexity of the same algorithmperforming on vector space model using vector similarity measure as distance.
3
Content available Content-based image retrieval using a signature graph and a self-organizing map
100%
Van T. , Le T.
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tom 26
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nr 2
423-438
EN
In order to effectively retrieve a large database of images, a method of creating an image retrieval system CBIR (contentbased image retrieval) is applied based on a binary index which aims to describe features of an image object of interest. This index is called the binary signature and builds input data for the problem of matching similar images. To extract the object of interest, we propose an image segmentation method on the basis of low-level visual features including the color and texture of the image. These features are extracted at each block of the image by the discrete wavelet frame transform and the appropriate color space. On the basis of a segmented image, we create a binary signature to describe the location, color and shape of the objects of interest. In order to match similar images, we provide a similarity measure between the images based on binary signatures. Then, we present a CBIR model which combines a signature graph and a self-organizing map to cluster and store similar images. To illustrate the proposed method, experiments on image databases are reported, including COREL, Wang and MSRDI.
4
Content available remote Fitness-distance analysis of a car sequencing problem
100%
Kubiak M. , Jaszkiewicz A. , Kominek P.
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tom Vol. 31, No. 3-4
263-276
EN
The paper describes a fitness-distance analysis of a car sequencing problem. It defines 5 similarity measures for solutions of the problem, describes computational experiments and provides values of determination coefficients between fitness and similarity, which are an indicator of fitness-distance correlation (or 'big valley'). The analysis reveals certain correlations of fitness and two types of similarity for 4 of 5 types of available instances. This results might motivate such designs of metaheuristics for these types of instances which would exploit the structure in fitness landscapes.
5
Content available Hydraulic model based method for leaks location in urban water distribution network
100%
Tomasik P.
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tom nr 1(61)
45-50
EN
The paper describes the use one of the methods for locating leaks based on an accurate model of the network. The paper presents the results of this approach for the active experiment which was carried out on the water supply. The experiment consisted of releasing water with the varying intensity of the fire hydrants located on the test area. Based on the readings of flow meters located in the district, the aim was to identify pre-defined areas in which the simulated leaks occurred. The results are summarized in conclusions.
6
Content available Method for identification of fragments of lip prints images on the basis of the generalized hough transform
88%
Wrobel K. , Doroz R.
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tom Vol. 22
189--193
EN
The paper presents a new method for identification of fragments of lip print images on the basis of the Generalized Hough Transform (GHT). The effectiveness of this method was verified in practice. The maximum value obtained from the accumulator array after the Hough transform has been assumed as the measure of similarity between a lip print image and a reference object. The advantage of this method is the possibility of use in forensic science to identify persons who left lip prints at crime scenes.
7
Content available Efficient face recognition based on weighted matrix distance metrics and 2DPCA algorithm
88%
Rouabhia C. , Tebbikh H.
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tom Vol. 21, no. 2
207-221
EN
In this paper, a new similarity measure is developed for human face recognition, namely, weighted matrix distance. The key difference between this metric and the standard distances is the use of matrices and weights rather than the vectors only. The two feature matrices are obtained by two-dimensional principal component analysis (2DPCA). The weights are the inverse of the eigenvalues sorted in decreasing order of the covariance matrix of all training face matrices. Experiments are performed under illumination and facial expression variations using four face image databases: ORL, Yale, PF01 and a subset of FERET. The results demonstrate the effectiveness of the proposed weighted matrix distances in 2DPCA face recognition over the standard matrix distance metrics: Yang, Frobenius and assembled matrix distance (AMD).
8
Content available remote Interval-valued Fuzzy Soft Decision Making Methods Based on MABAC, Similarity Measure and EDAS
88%
Peng X. , Dai J. , Yuan H.
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tom Vol. 152, nr 4
373--396
EN
Interval-valued fuzzy soft decision making problems have obtained great popularity recently. Most of the current methods depend on level soft set that provide choice value of alternatives to be ranked. Such choice value always encounter the equal condition that the optimal alternative can't be gained. Most important of all, the current decision making procedure is not in accordance with the way that the decision makers think about the decision making problems. In this paper, we initiate a new axiomatic definition of interval-valued fuzzy distance measure and similarity measure, which is expressed by interval-valued fuzzy number (IVFN) that will reduce the information loss and keep more original information. Later, the objective weights of various parameters are determined via grey system theory, meanwhile, we develop the combined weights, which can show both the subjective information and the objective information. Then, we present three algorithms to solve interval-valued fuzzy soft decision making problems by Multi- Attributive Border Approximation area Comparison (MABAC), Evaluation based on Distance from Average Solution (EDAS) and new similarity measure. Three approaches solve some unreasonable conditions and promote the development of decision making methods. Finally, the effectiveness and feasibility of approaches are demonstrated by some numerical examples.
9
Content available Content-based image retrieval using a signature graph and a self-organizing map
75%
Van T. T. , Le T. M.
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2016
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tom Vol. 26, no. 2
423--438
EN
In order to effectively retrieve a large database of images, a method of creating an image retrieval system CBIR (content-based image retrieval) is applied based on a binary index which aims to describe features of an image object of interest. This index is called the binary signature and builds input data for the problem of matching similar images. To extract the object of interest, we propose an image segmentation method on the basis of low-level visual features including the color and texture of the image. These features are extracted at each block of the image by the discrete wavelet frame transform and the appropriate color space. On the basis of a segmented image, we create a binary signature to describe the location, color and shape of the objects of interest. In order to match similar images, we provide a similarity measure between the images based on binary signatures. Then, we present a CBIR model which combines a signature graph and a self-organizing map to cluster and store similar images. To illustrate the proposed method, experiments on image databases are reported, including COREL, Wang and MSRDI.
10
Wybór miary podobieństwa a efektywność grupowania reguł w złożonych bazach wiedzy
75%
Nowak-Brzezińska A. , Wakulicz-Deja A.
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tom Vol. 31, nr 2A
189-201
PL
Praca przedstawia wyniki eksperymentów analizujących wpływ wybranej metryki podobieństwa na efektywność grupowania reguł w złożonych bazach wiedzy. Inna metryka podobieństwa to inna struktura skupień reguł, a co za tym idzie, inny przebieg procesów wnioskowania dla takich struktur.
EN
The paper presents the results of the experiments analyzing an influence choosed similarity measure on the efficiency of rules (from composited knowledge bases) clustering. In authors oppinion. Each time when we use different measure we achieving different structure of rules' clusters and the results of inference process.
11
Content available Mutual information based registration of brain images
75%
Kuczyński K. , Mikołajczak P.
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tom Vol. 3
MI213--219
EN
Registration is one of the essential medical image processing techniques. The goal is to find a geometric transformation, that relates corresponding voxels in two different 3D images of the same object. The publication presents a registration technique based on maximization of mutual information.
12
Content available Automatic alignment of intramodal tomographic data using s-distance approach
63%
Król Z.
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tom Vol. 6
IT3--11
EN
The alignment of volumetric datasets is an important problem in the processing of medical data. It is a prerequisite to numerous image based applications in diagnostic and therapeutic routines. In this paper, a new method is proposed for matching of 3D intramodality medical images. Our approach is based on some generalization of feature distance definition. Analogous to the standard surface matching, our algorithm uses also the chamfer distance like metric to define the quality of match function, however, the evaluation of the distance map is performed in a different way. The s-distance method is a step towards an automatic extraction of features, where each feature’s role in the registration process is weighted based on its relative statistical or spatial significance. As an alternative to the user-dependent non-automatic registration methods this approach offers a good assessment of similarity in the intramodality case. The elimination of less significant features in the registration process has resulted in a greatly improved efficiency over the voxel-based methods. Studying certain properties of the search space topography provides some insights into the performance of the proposed method as well as the standard registration algorithms in the rigid body registration problem.
13
Content available Comparative investigations of similarity measure exploited in medical images preselection
63%
Wróbel K. , Porwik P.
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2004
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tom Vol. 8
II25--31
EN
Nowadays, the most significant impact of digital image processing in the area of applications are real–world problems. Many new technological trends in medicine and digital processing have been implemented. Several factors indicate such development. A major one is the perpetually declining cost of the computer equipment required. Both processing unit and capacity of storage devices continue to become less expensive year by year. Another factor is the increasing availability of equipment for digitising and displaying images. In modern image processing, images have to be compared each other because such approach allows us to automate of retrieval process. Computer image retrieving is today especially important in medical diagnostics [1,7] or in preliminary images selection [8,9]. Today, in the digital image processing are used techniques and methods which have well known mathematical backgrounds. It can be observed, that in the area of digital signal processing, the Hough and well known the Fourier transform are exploited very often. These transforms are frequently use in image retrieving and can be implemented as computer applications. In many cases the mentioned methods give promising results in images classification or preselection [1,2,4,5,11]. Special properties of such transforms can be used in statistical or comparative goals, especially when searched information has graphic form. Taking into account the mentioned applications, transforms as methods of preliminary medical images selection have been investigated. From this reason pictures, analysing in the paper, to medical images have been limited.
14
Podziałowe i hierarchiczne algorytmy grupowania danych
63%
Stąpor K.
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tom Vol. 25, nr 1
157-170
PL
W artykule przedstawiono najważniejsze podziałowe oraz hierarchiczne algorytmy grupowania danych. Wśród algorytmów podziałowych omówiono algorytmy oparte na prototypach punktowych oraz liniowych. Przedstawiono algorytmy hierarchiczne dla różnych miar podobieństwa oraz omówiono skrótowo kla-steryzację neuronową wykorzystującą sieć Kohonena.
EN
In this paper the most important partitional and hierarchical data clustering algorithms arę described. Among partitional algorithms those based on point and linear prototypes arę discussed. In hierarchical algorithms different similarity measures arę described. Also, neural network clustering based on Kohonen network is described.
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Content available remote Similarity detection based on document matrix model and edit distance algorithm
51%
Niewiarowski A.
Computer Assisted Methods in Engineering and Science
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2019
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tom vol. 26, no. 3-4
163--175
EN
This paper presents a new algorithm with an objective of analyzing the similarity measure between two text documents. Specifically, the main idea of the implemented method is based on the structure of the so-called “edit distance matrix” (similarity matrix). Elements of this matrix are filled with a formula based on Levenshtein distances between sequences of sentences. The Levenshtein distance algorithm (LDA) is used as a replacement for various implementations of stemming or lemmatization methods. Additionally, the proposed algorithm is fast, precise, and may be implemented for analyzing very large documents (e.g., books, diploma works, newspapers, etc.). Moreover, it seems to be versatile for the most common European languages such as Polish, English, German, French and Russian. The presented tool is intended for all employees and students of the university to detect the level of similarity regarding analyzed documents. Results obtained in the paper were confirmed in the tests shown in the article.
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Przydatność wybranych miar podobieństwa dla danych binarnych do analiz wielocechowych w badaniach molekularnych
51%
Mankowski D. R. , Laudanski Z. , Janaszek M.
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nr 262
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