Abstract-One of the major problems in texture analysis is segmenting images into different regions based on textures. In this paper, we present a new approach of texture segmentation, which is based on both Kohonen maps and mathematical morphology, using three different texture features, namely, Haralick features based on gray-level co-occurrence matrix (GLCM), fractal features based on fractal dimension using the differential box counting method, and wavelet features based on wavelet transform. These features are used to train the Kohonen Network, which will be represented by the underlying probability density function (PDF). The segmentation of this map's representation is made by morphological watershed transformation. In the final part of our algorithm, this will help on the segmentation of the textural image, by assigning each pixel to a modal region extracted from the map. Our work covers the results obtained by the three extraction methods taking into consideration the execution time and the error rate.
Nowadays, the online environment is extra information-rich and allows companies to offer and receive more and more options and opportunities in multiple areas. Thus, decision-makers have abundantly available alternatives to choose from the best one or rank from the most to the least preferred. However, in the multicriteria decision-making field, most tools support a limited number of alternatives with as narrow criteria as possible. Decision-makers are forced to apply a screening or filtering method to reduce the size of the problem, which will slow down the process and eliminate some potential alternatives from the rest of the decision-making process. Implementing MCDM methods in high-performance parallel and distributed computing environments becomes crucial to ensure the scalability of multicriteria decision-making solutions in Big Data contexts, where one can consider a vast number of alternatives, each being described on the basis of a number of criteria.In this context, we consider TOPSIS one of the most widely used MCDM methods. We present a parallel implementation of TOPSIS based on the MapReduce paradigm. This solution will reduce the response time of the decision-making process and facilitate the analysis of the robustness and sensitivity of the method in a high-dimension problem at a reasonable response time.Three multicriteria analysis problems were evaluated to show the proposed approach's computational efficiency and performance. All experiments are carried out within GCP's Dataproc, a service allowing the execution of Apache Hadoop and Spark tasks in Google Cloud. The results of the tests obtained are very significant and promising.
In this paper, we present a collaborative multi-agent based system for data mining. We have used two data mining model functions, clustering of variables in order to build homogeneous groups of attributes, association rules inside each of these groups and a multi-agent approach to integrate the both data mining techniques. For the association rules extraction, we use both apriori algorithm and genetic algorithm. The main goal of this paper is the evaluation of the association rules obtained by running apriori and genetic algorithm using quantitative datasets in multi agent environment.
Abstract. This article proposes a hybrid approach for texture-based image classification using the gray-level co-occurrence matrices (GLCM), self-organizing map (SOM) methods and mathematical morphology in an unsupervised context. The GLCM is a matrix of how often different combinations of pixel brightness values (grey levels) occur in an image. The GLCM matrices extracted from an image are processed to create the training data set for a SOM neural network. The SOM model organizes and extracts prototypes from various features obtained from the GLCM matrices. These prototypes are represented by the underlying probability density function (pdf). Under the assumption that each modal region of the underlying pdf corresponds to a one homogenous region in the texture image, the second part of the approach consists in partitioning the self-organizing map into connected modal regions by making concepts of morphological watershed transformation suitable for their detection. The classification process is then based on the so detected modal regions. We compare this approach to other texture feature extraction using fractal dimension.
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