Improve Color Image Clustering with Using Combination of GLS, Giza Pyramid Construction and K-Means Algorithm
الموضوعات : Journal of Computer & Robotics
Masood Shahrian
1
,
Madjid Khalilian
2
1 - Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
2 - Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran
الکلمات المفتاحية: Image processing, Color Image clustering, Giza Pyramid Construction, GLS, k-means Algorithm,
ملخص المقالة :
Color image clustering is recognized as a complex challenge in the field of image processing. To improve the results of image clustering, meta-heuristic optimization algorithms can be employed. These algorithms are typically straightforward and can efficiently tackle problems in a short time frame, which offers distinct advantages. However, as the complexity of the problem increases, the solutions derived from these algorithms often fail to represent the optimal solution, resulting in limitations for their practical use. Thus, improving the performance and accuracy of existing algorithms is essential for broadening their applicability. Many meta-heuristic algorithms struggle to maintain an appropriate balance between exploration and exploitation during their update processes, and this issue has not been sufficiently addressed. In this research, we present a novel approach to image clustering. Our method integrates an enhanced Giza Pyramid Construction (GPC) with the Guided Learning Strategy (GLS) and k-means clustering. The GLS strategy assesses the standard deviation of historical positions of individuals across recent generations to evaluate population dispersion and deduce the type of guidance the algorithm requires at any given time. When the algorithm leans towards exploration, this strategy steers it towards exploitation, and vice versa. By identifying and addressing the algorithm’s current needs, this strategy can significantly improve the performance of various optimization algorithms. Furthermore, the Giza Pyramid Construction, inspired by the historical practices of ancient Egypt, mathematically models the behavior of worker groups engaged in constructing large pyramids. We assess the effectiveness of our proposed algorithm in the context of color image clustering and compare the results against several established evaluators that can analyze internal cluster evaluations and inter-cluster distances. Our findings demonstrate that the proposed method achieves superior results compared to other state-of-the-art techniques, based on both objective and subjective evaluation metrics.
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