Automatic Superpixel-Based Clustering for Color Image Segmentation Using q-Generalized Pareto Distribution under Linear Normalization and Hunger Games Search

Mohamed Abd Elaziz, Esraa Osama Abo Zaid, Mohammed A. A. Al-qaness and Rehab Ali Ibrahim
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Mohamed Abd Elaziz: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
Esraa Osama Abo Zaid: Department of Mathematics, Faculty of Science, Seuz University, Suez 41522, Egypt
Mohammed A. A. Al-qaness: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Rehab Ali Ibrahim: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt

Mathematics, 2021, vol. 9, issue 19, 1-18

Abstract: Superixel is one of the most efficient of the image segmentation approaches that are widely used for different applications. In this paper, we developed an image segmentation based on superpixel and an automatic clustering using q-Generalized Pareto distribution under linear normalization (q-GPDL), called ASCQPHGS. The proposed method uses the superpixel algorithm to segment the given image, then the Density Peaks clustering (DPC) is employed to the results obtained from the superpixel algorithm to produce a decision graph. The Hunger games search (HGS) algorithm is employed as a clustering method to segment the image. The proposed method is evaluated using two different datasets, collected form Berkeley segmentation dataset and benchmark (BSDS500) and standford background dataset (SBD). More so, the proposed method is compared to several methods to verify its performance and efficiency. Overall, the proposed method showed significant performance and it outperformed all compared methods using well-known performance metrics.

Keywords: superpixel; image segmentation; hunger games search (HGS); density peaks clustering; q-generalized pareto distribution under linear normalization (q-GPDL) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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