<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The need for information is gradually shifting from text to images due to the technology’s growth and increase in digital images. It is quite challenging for people to find similar color images. To obtain similarity matching, the color of the image needs to be identified. This paper aims at various clustering techniques to identify the color of the digital image. Though many clustering techniques exist, this paper focuses on Fuzzy c-Means, Mean-Shift, and a hybrid technique that amalgamates the agglomerative hierarchies and k-Means, known as hKmeans to cluster the intensity of the image. Applying evaluation metrics of Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Homogeneity, Completeness, V-Score, and Peak signal-to-noise ratio it is proven that the results obtained demonstrate the good performance of the proposed technique. Then the color histogram is applied to identify the color and differentiate the color distribution on the original and clustered image.</p>
</abstract>

The need for information is gradually shifting from text to images due to the technology’s growth and increase in digital images. It is quite challenging for people to find similar color images. To obtain similarity matching, the color of the image needs to be identified. This paper aims at various clustering techniques to identify the color of the digital image. Though many clustering techniques exist, this paper focuses on Fuzzy c-Means, Mean-Shift, and a hybrid technique that amalgamates the agglomerative hierarchies and k-Means, known as hKmeans to cluster the intensity of the image. Applying evaluation metrics of Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Homogeneity, Completeness, V-Score, and Peak signal-to-noise ratio it is proven that the results obtained demonstrate the good performance of the proposed technique. Then the color histogram is applied to identify the color and differentiate the color distribution on the original and clustered image.

Image Clustering and Feature Extraction by Utilizing an Improvised Unsupervised Learning Approach

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The need for information is gradually shifting from text to images due to the technology’s growth and increase in digital images. It is quite challenging for...

Image Clustering and Feature Extraction by Utilizing an Improvised Unsupervised Learning Approach

Publié en ligne: 12 juin 2023

Pages: 3 - 19

Reçu: 05 oct. 2022

Accepté: 14 avr. 2023

DOI: https://doi.org/10.2478/cait-2023-0010

Mots clés
Color histogram, evaluation metrics, feature extraction, image clustering, hybrid clustering

© 2023 R. Bhuvanya et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Image Clustering and Feature Extraction by Utilizing an Improvised Unsupervised Learning Approach

Publié en ligne: 12 juin 2023

Pages: 3 - 19

Reçu: 05 oct. 2022

Accepté: 14 avr. 2023

DOI: https://doi.org/10.2478/cait-2023-0010

Mots clésColor histogram, evaluation metrics, feature extraction, image clustering, hybrid clustering

© 2023 R. Bhuvanya et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Color histogram, evaluation metrics, feature extraction, image clustering, hybrid clustering