The Experts below are selected from a list of 6381 Experts worldwide ranked by ideXlab platform
J.f. Peters - One of the best experts on this subject based on the ideXlab platform.
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voronoi region based adaptive unsupervised color image segmentation
Pattern Recognition, 2017Co-Authors: R Hettiarachchi, J.f. PetersAbstract:Color image segmentation is a crucial step in many computer vision and pattern recognition applications. This paper introduces an adaptive and unsupervised approach based on Voronoi regions to solve the color image segmentation problem. The proposed method uses a hybrid of spatial and feature space Dirichlet tessellation followed by inter-Voronoi region proximal Cluster merging to automatically find the number of Clusters and Cluster Centroids in an image. Since, the Voronoi regions are much smaller compared to the whole image, Voronoi region-wise Clustering improves the efficiency and accuracy of the number of Clusters and Cluster Centroid estimation process. The proposed method was compared with four other adaptive unsupervised Cluster-based image segmentation algorithms on three image segmentation evaluation benchmarks. The experimental results reported in this paper confirm that the proposed method outperforms the existing algorithms in terms of the image segmentation quality and results in much lower average execution time per image. HighlightsWe propose a hybrid of Dirichlet tessellation to automatically segment an image.Spatial Dirichlet tessellation adaptively divides an image into Voronoi regions.Feature space Dirichlet tessellation adaptively Clusters pixels in Voronoi regions.Inter-Voronoi region proximal Cluster merging automatically finds the final Clusters.
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voronoi region based adaptive unsupervised color image segmentation
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: R Hettiarachchi, J.f. PetersAbstract:Color image segmentation is a crucial step in many computer vision and pattern recognition applications. This article introduces an adaptive and unsupervised Clustering approach based on Voronoi regions, which can be applied to solve the color image segmentation problem. The proposed method performs region splitting and merging within Voronoi regions of the Dirichlet Tessellated image (also called a Voronoi diagram) , which improves the efficiency and the accuracy of the number of Clusters and Cluster Centroids estimation process. Furthermore, the proposed method uses Cluster Centroid proximity to merge proximal Clusters in order to find the final number of Clusters and Cluster Centroids. In contrast to the existing adaptive unsupervised Cluster-based image segmentation algorithms, the proposed method uses K-means Clustering algorithm in place of the Fuzzy C-means algorithm to find the final segmented image. The proposed method was evaluated on three different unsupervised image segmentation evaluation benchmarks and its results were compared with two other adaptive unsupervised Cluster-based image segmentation algorithms. The experimental results reported in this article confirm that the proposed method outperforms the existing algorithms in terms of the quality of image segmentation results. Also, the proposed method results in the lowest average execution time per image compared to the existing methods reported in this article.
R Hettiarachchi - One of the best experts on this subject based on the ideXlab platform.
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voronoi region based adaptive unsupervised color image segmentation
Pattern Recognition, 2017Co-Authors: R Hettiarachchi, J.f. PetersAbstract:Color image segmentation is a crucial step in many computer vision and pattern recognition applications. This paper introduces an adaptive and unsupervised approach based on Voronoi regions to solve the color image segmentation problem. The proposed method uses a hybrid of spatial and feature space Dirichlet tessellation followed by inter-Voronoi region proximal Cluster merging to automatically find the number of Clusters and Cluster Centroids in an image. Since, the Voronoi regions are much smaller compared to the whole image, Voronoi region-wise Clustering improves the efficiency and accuracy of the number of Clusters and Cluster Centroid estimation process. The proposed method was compared with four other adaptive unsupervised Cluster-based image segmentation algorithms on three image segmentation evaluation benchmarks. The experimental results reported in this paper confirm that the proposed method outperforms the existing algorithms in terms of the image segmentation quality and results in much lower average execution time per image. HighlightsWe propose a hybrid of Dirichlet tessellation to automatically segment an image.Spatial Dirichlet tessellation adaptively divides an image into Voronoi regions.Feature space Dirichlet tessellation adaptively Clusters pixels in Voronoi regions.Inter-Voronoi region proximal Cluster merging automatically finds the final Clusters.
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voronoi region based adaptive unsupervised color image segmentation
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: R Hettiarachchi, J.f. PetersAbstract:Color image segmentation is a crucial step in many computer vision and pattern recognition applications. This article introduces an adaptive and unsupervised Clustering approach based on Voronoi regions, which can be applied to solve the color image segmentation problem. The proposed method performs region splitting and merging within Voronoi regions of the Dirichlet Tessellated image (also called a Voronoi diagram) , which improves the efficiency and the accuracy of the number of Clusters and Cluster Centroids estimation process. Furthermore, the proposed method uses Cluster Centroid proximity to merge proximal Clusters in order to find the final number of Clusters and Cluster Centroids. In contrast to the existing adaptive unsupervised Cluster-based image segmentation algorithms, the proposed method uses K-means Clustering algorithm in place of the Fuzzy C-means algorithm to find the final segmented image. The proposed method was evaluated on three different unsupervised image segmentation evaluation benchmarks and its results were compared with two other adaptive unsupervised Cluster-based image segmentation algorithms. The experimental results reported in this article confirm that the proposed method outperforms the existing algorithms in terms of the quality of image segmentation results. Also, the proposed method results in the lowest average execution time per image compared to the existing methods reported in this article.
Giuseppe Manco - One of the best experts on this subject based on the ideXlab platform.
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PKDD - Clustering Transactional Data
Principles of Data Mining and Knowledge Discovery, 2002Co-Authors: Fosca Giannotti, Cristian Gozzi, Giuseppe MancoAbstract:In this paper we present a partitioning method capable to manage transactions, namelyt uples of variable size of categorical data. We adapt the standard definition of mathematical distance used in the K- Means algorithm to represent dissimilarityam ong transactions, and redefine the notion of Cluster Centroid. The Cluster Centroid is used as the representative of the common properties of Cluster elements. We show that using our concept of Cluster Centroid together with Jaccard distance we obtain results that are comparable in qualityw ith the most used transactional Clustering approaches, but substantial improve their efficiency.
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Clustering transactional data
European Conference on Principles of Data Mining and Knowledge Discovery, 2002Co-Authors: Fosca Giannotti, Cristian Gozzi, Giuseppe MancoAbstract:In this paper we present a partitioning method capable to manage transactions, namelyt uples of variable size of categorical data. We adapt the standard definition of mathematical distance used in the K- Means algorithm to represent dissimilarityam ong transactions, and redefine the notion of Cluster Centroid. The Cluster Centroid is used as the representative of the common properties of Cluster elements. We show that using our concept of Cluster Centroid together with Jaccard distance we obtain results that are comparable in qualityw ith the most used transactional Clustering approaches, but substantial improve their efficiency.
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SEBD - Clustering Transactional Data.
2001Co-Authors: Fosca Giannotti, Cristian Gozzi, Giuseppe MancoAbstract:In this paper we present a partitioning method capable to manage transactions, namelyt uples of variable size of categorical data. We adapt the standard definition of mathematical distance used in the K- Means algorithm to represent dissimilarityam ong transactions, and redefine the notion of Cluster Centroid. The Cluster Centroid is used as the representative of the common properties of Cluster elements. We show that using our concept of Cluster Centroid together with Jaccard distance we obtain results that are comparable in qualityw ith the most used transactional Clustering approaches, but substantial improve their efficiency.
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ITCC - Characterizing Web user accesses: a transactional approach to Web log Clustering
Proceedings. International Conference on Information Technology: Coding and Computing, 1Co-Authors: Fosca Giannotti, Cristian Gozzi, Giuseppe MancoAbstract:We present a partitioning method able to manage Web log sessions. Sessions are assimilable to transactions, i.e., tuples of variable size of categorical data. We adapt the standard definition of mathematical distance used in the K-Means algorithm to represent transactions dissimilarity, and redefine the notion of Cluster Centroid. The Cluster Centroid is used as the representative of the common properties of Cluster elements. We show that using our concept of Cluster Centroid together with Jaccard distance we obtain results that are comparable with standard approaches, but substantially improve their efficiency.
Fosca Giannotti - One of the best experts on this subject based on the ideXlab platform.
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PKDD - Clustering Transactional Data
Principles of Data Mining and Knowledge Discovery, 2002Co-Authors: Fosca Giannotti, Cristian Gozzi, Giuseppe MancoAbstract:In this paper we present a partitioning method capable to manage transactions, namelyt uples of variable size of categorical data. We adapt the standard definition of mathematical distance used in the K- Means algorithm to represent dissimilarityam ong transactions, and redefine the notion of Cluster Centroid. The Cluster Centroid is used as the representative of the common properties of Cluster elements. We show that using our concept of Cluster Centroid together with Jaccard distance we obtain results that are comparable in qualityw ith the most used transactional Clustering approaches, but substantial improve their efficiency.
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Clustering transactional data
European Conference on Principles of Data Mining and Knowledge Discovery, 2002Co-Authors: Fosca Giannotti, Cristian Gozzi, Giuseppe MancoAbstract:In this paper we present a partitioning method capable to manage transactions, namelyt uples of variable size of categorical data. We adapt the standard definition of mathematical distance used in the K- Means algorithm to represent dissimilarityam ong transactions, and redefine the notion of Cluster Centroid. The Cluster Centroid is used as the representative of the common properties of Cluster elements. We show that using our concept of Cluster Centroid together with Jaccard distance we obtain results that are comparable in qualityw ith the most used transactional Clustering approaches, but substantial improve their efficiency.
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SEBD - Clustering Transactional Data.
2001Co-Authors: Fosca Giannotti, Cristian Gozzi, Giuseppe MancoAbstract:In this paper we present a partitioning method capable to manage transactions, namelyt uples of variable size of categorical data. We adapt the standard definition of mathematical distance used in the K- Means algorithm to represent dissimilarityam ong transactions, and redefine the notion of Cluster Centroid. The Cluster Centroid is used as the representative of the common properties of Cluster elements. We show that using our concept of Cluster Centroid together with Jaccard distance we obtain results that are comparable in qualityw ith the most used transactional Clustering approaches, but substantial improve their efficiency.
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ITCC - Characterizing Web user accesses: a transactional approach to Web log Clustering
Proceedings. International Conference on Information Technology: Coding and Computing, 1Co-Authors: Fosca Giannotti, Cristian Gozzi, Giuseppe MancoAbstract:We present a partitioning method able to manage Web log sessions. Sessions are assimilable to transactions, i.e., tuples of variable size of categorical data. We adapt the standard definition of mathematical distance used in the K-Means algorithm to represent transactions dissimilarity, and redefine the notion of Cluster Centroid. The Cluster Centroid is used as the representative of the common properties of Cluster elements. We show that using our concept of Cluster Centroid together with Jaccard distance we obtain results that are comparable with standard approaches, but substantially improve their efficiency.
Dinesh Kumar - One of the best experts on this subject based on the ideXlab platform.
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Automatic Clustering using quantum-based multi-objective emperor penguin optimizer and its applications to image segmentation
Modern Physics Letters A, 2019Co-Authors: Dinesh Kumar, Vijay Kumar, Rajani KumariAbstract:In this study, a novel quantum-based multi-objective is proposed using Schrodinger equations. The two new operations namely weighted Cluster Centroid computation and threshold setting are also intr...
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automatic Cluster evolution using gravitational search algorithm and its application on image segmentation
Engineering Applications of Artificial Intelligence, 2014Co-Authors: Vijay Kumar, Jitender Kumar Chhabra, Dinesh KumarAbstract:In real life problems, prior information about the number of Clusters is not known. In this paper, an attempt has been made to determine the number of Clusters using automatic Clustering using gravitational search algorithm (ACGSA). Based on the statistical property of datasets, two new concepts are proposed to efficiently find the optimal number of Clusters. Within the ACGSA, a variable chromosome representation is used to encode the Cluster centers with different number of Clusters. In order to refine Cluster Centroids, two new operations namely threshold setting and weighted Cluster Centroid computation are also introduced. Finally, a new fitness function is proposed to make the search more efficient. A comparison of the proposed technique is also carried out with automatic Clustering techniques developed recently. The proposed technique is further applied for automatic segmentation of both grayscale and color images and its performance is compared with other techniques. Experimental results demonstrate the efficiency and efficacy of the proposed Clustering technique over other existing techniques.