Texture segmentation based on Laguerre Gauss functions and k-means algorithm driven by Kullback–Leibler divergence

Costantini, Luca; Capodiferro, Licia; Carli, Marco; Neri, Alessandro

doi:10.1117/1.jei.22.4.043015

Cited by 5 publications

(3 citation statements)

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“…If k≠1, go to step 1 and continue the adjustment process. Otherwise, terminate the iteration and update all centroid points for new cluster members with a criterion of minimizing the following term [27][28]:…”

Section: Procedures Of the Proposed Clustering Methodsmentioning

confidence: 99%

“…However, the term "k-means" was first used by James MacQueen in 1967 [25][26][27]. It is one of the simplest yet robust deterministic clustering algorithms, which aims to partition N observations or data samples into k user-defined clusters where each observation belongs to the cluster with the nearest mean that is regarded as a prototype of the corresponding cluster [27][28]. The flowchart of the well known k-means approach achieved by Stuart Lloyd is presented in Fig.…”

Section: K-means Clustering Algorithmmentioning

confidence: 99%

“…In fact, the above-mentioned k-means algorithm attempts to minimize the within-cluster sum of squares. In other words, the k-means technique seeks to minimize the following term [27][28][29]:…”

Section: K-means Clustering Algorithmmentioning

confidence: 99%

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K-means based Clustering Method with a Fixed Number of Cluster Members

Moon²

2014

Journal of Korea Multimedia Society

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Clustering methods are very useful in many fields such as data mining, classification, and object recognition. Both the supervised and unsupervised grouping approaches can classify a series of sample data with a predefined or automatically assigned cluster number. However, there is no constraint on the number of elements for each cluster. Numbers of cluster members for each cluster obtained from clustering schemes are usually random. Thus, some clusters possess a large number of elements whereas others only have a few members. In some areas such as logistics management, a fixed number of members are preferred for each cluster or logistic center. Consequently, it is necessary to design a clustering method that can automatically adjust the number of group elements. In this paper, a k-means based clustering method with a fixed number of cluster members is proposed. In the proposed method, first, the data samples are clustered using the k-means algorithm. Then, the number of group elements is adjusted by employing a greedy strategy. Experimental results demonstrate that the proposed clustering scheme can classify data samples efficiently for a fixed number of cluster members.

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Section: Procedures Of the Proposed Clustering Methodsmentioning

confidence: 99%