Next Article in Journal
Pointwise Partial Information Decomposition Using the Specificity and Ambiguity Lattices
Previous Article in Journal
A Novel Algorithm to Improve Digital Chaotic Sequence Complexity through CCEMD and PE
Article

Image Clustering with Optimization Algorithms and Color Space

1
Computer Engineering Department, Technology Faculty, Gazi University, Ankara 06500, Turkey
2
Department of Computer Engineering, University of Tabriz, Tabriz 51666, Iran
*
Authors to whom correspondence should be addressed.
Received: 19 March 2018 / Revised: 13 April 2018 / Accepted: 15 April 2018 / Published: 18 April 2018
In image clustering, it is desired that pixels assigned in the same class must be the same or similar. In other words, the homogeneity of a cluster must be high. In gray scale image segmentation, the specified goal is achieved by increasing the number of thresholds. However, the determination of multiple thresholds is a typical issue. Moreover, the conventional thresholding algorithms could not be used in color image segmentation. In this study, a new color image clustering algorithm with multilevel thresholding has been presented and, it has been shown how to use the multilevel thresholding techniques for color image clustering. Thus, initially, threshold selection techniques such as the Otsu and Kapur methods were employed for each color channel separately. The objective functions of both approaches have been integrated with the forest optimization algorithm (FOA) and particle swarm optimization (PSO) algorithm. In the next stage, thresholds determined by optimization algorithms were used to divide color space into small cubes or prisms. Each sub-cube or prism created in the color space was evaluated as a cluster. As the volume of prisms affects the homogeneity of the clusters created, multiple thresholds were employed to reduce the sizes of the sub-cubes. The performance of the proposed method was tested with different images. It was observed that the results obtained were more efficient than conventional methods. View Full-Text
Keywords: image clustering; color space; thresholding image clustering; color space; thresholding
Show Figures

Figure 1

MDPI and ACS Style

Rahkar Farshi, T.; Demirci, R.; Feizi-Derakhshi, M.-R. Image Clustering with Optimization Algorithms and Color Space. Entropy 2018, 20, 296. https://0-doi-org.brum.beds.ac.uk/10.3390/e20040296

AMA Style

Rahkar Farshi T, Demirci R, Feizi-Derakhshi M-R. Image Clustering with Optimization Algorithms and Color Space. Entropy. 2018; 20(4):296. https://0-doi-org.brum.beds.ac.uk/10.3390/e20040296

Chicago/Turabian Style

Rahkar Farshi, Taymaz, Recep Demirci, and Mohammad-Reza Feizi-Derakhshi. 2018. "Image Clustering with Optimization Algorithms and Color Space" Entropy 20, no. 4: 296. https://0-doi-org.brum.beds.ac.uk/10.3390/e20040296

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop