Intelligent Image Clustering
نویسندگان
چکیده
We highlight a partition clustering method, which proposes an experimental solution to the famous problem of automatic discovery of the number of clusters (k). The majority of partition clustering methods consider the manual valuation of k. Manual valuation of k may be interesting for specific domains of applications where the expert has an accurate idea of the number of clusters he wants, however it is unrealistic for generic applications, and needs important estimation efforts without any insurance of their efficiencies.
منابع مشابه
Clustering with Intelligent Linexk-Means
The intelligent LINEX k-means clustering is a generalization of the k-means clustering so that the number of clusters and their related centroid can be determined while the LINEX loss function is considered as the dissimilarity measure. Therefore, the selection of the centers in each cluster is not randomly. Choosing the LINEX dissimilarity measure helps the researcher to overestimate or undere...
متن کاملIntelligent scalable image watermarking robust against progressive DWT-based compression using genetic algorithms
Image watermarking refers to the process of embedding an authentication message, called watermark, into the host image to uniquely identify the ownership. In this paper a novel, intelligent, scalable, robust wavelet-based watermarking approach is proposed. The proposed approach employs a genetic algorithm to find nearly optimal positions to insert watermark. The embedding positions coded as chr...
متن کاملElectrofacies clustering and a hybrid intelligent based method for porosity and permeability prediction in the South Pars Gas Field, Persian Gulf
This paper proposes a two-step approach for characterizing the reservoir properties of the world’s largest non-associated gas reservoir. This approach integrates geological and petrophysical data and compares them with the field performance analysis to achieve a practical electrofacies clustering. Porosity and permeability prediction is done on the basis of linear functions, succeeding the elec...
متن کاملImage Segmentation: Type–2 Fuzzy Possibilistic C-Mean Clustering Approach
Image segmentation is an essential issue in image description and classification. Currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. Moreover, there are many uncertainties and vagueness in images, which crisp clustering and even Type-1 fuzzy clustering could not handle. Hence, Type-...
متن کاملEntropy-based Consensus for Distributed Data Clustering
The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...
متن کاملDetection and separation of generic-shaped objects by fuzzy clustering
Purpose Existing shape-based fuzzy clustering algorithms are all designed to explicitly segment regular geometricallyshaped objects in an image, with the consequence that this restricts their capability to separate arbitrarily-shaped objects. Design/Methodology/Approach – With the aim of separating arbitrary shaped objects in an image, this paper presents a new detection and separation of gener...
متن کامل