Unsupervised Classification of Aerial Images Based on the Otsu's Method

Macedo-Cruz, Antonia; Villegas-Romero, Isidro; Santos-Peñas, Matilde; Pajares-Martinsanz, Gonzalo

doi:10.5772/33670

Cited by 2 publications

(4 citation statements)

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“…Dunn's index identifies the sets of clusters that are compact and well separated. The main goal of this measure is to maximize intercluster distances whilst minimizing intracluster distances [30]. The index is defined in [31].…”

Section: Methodsmentioning

confidence: 99%

“…Where d(c i , c j ) is the dissimilarity function between two clusters c i and c j and also diam(c) is the diameter of a cluster [31]. As such large values of D nc correspond to good clusters [30]. The implications of the Dunn's are the considerable amount of time required for its computation, and the sensitive to the presence of noise in data sets [31].…”

Section: Methodsmentioning

confidence: 99%

“…The implications of the Dunn's are the considerable amount of time required for its computation, and the sensitive to the presence of noise in data sets [31]. Davies-Bouldin index, As the Dunn's index, aims at identifying sets of clusters that are compact and well separated [30]. A similarity measure R ij between the clusters c i and c j is dened based on a measure of dispersion of a cluster c i and a dissimilarity measure between two clusters d ij .…”

Section: Methodsmentioning

confidence: 99%

“…, nc) and its most similar one [31]. Therefore, the cluster configuration that minimizes DB is taken as the optimal number of clusters [30]. Entropy determines cluster quality.…”

Section: Methodsmentioning

confidence: 99%

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Similarity Technique Effectiveness of Optimized Fuzzy C-means Clustering Based on Fuzzy Support Vector Machine for Noisy Data

Khanali

Vaziri

2021

Stat., optim. inf. comput.

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Fuzzy VIKOR C-means (FVCM) is a kind of unsupervised fuzzy clustering algorithm that improves the accuracyand computational speed of Fuzzy C-means (FCM). So it reduces the sensitivity to noisy and outlier data, and enhances performance and quality of clusters. Since FVCM allocates some data to a specific cluster based on similarity technique, reducing the effect of noisy data increases the quality of the clusters. This paper presents a new approach to the accurate location of noisy data to the clusters overcoming the constraints of noisy points through fuzzy support vector machine (FSVM), called FVCM-FSVM, so that at each stage samples with a high degree of membership are selected for training in the classification of FSVM. Then, the labels of the remaining samples are predicted so the process continues until the convergence of the FVCM-FSVM. The results of the numerical experiments showed the proposed approach has better performance than FVCM. Of course, it greatly achieves high accuracy.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…, nc) and its most similar one [31]. Therefore, the cluster configuration that minimizes DB is taken as the optimal number of clusters [30]. Entropy determines cluster quality.…”

Section: Methodsmentioning

confidence: 99%

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