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.
Highlights
Image Segmentation is a key component of various computer vision applications
Slime mould algorithm (SMA) [33], Barnacles mating optimizer (BMO) [34], atom search optimization (ASO) [35], ASO based on particle swarm optimization (ASOPSO) [36], Hunger games search (HGS) [37]
We compare the results of developed method with the modified version of FCM and Density Peaks clustering (DPC) using the QGEV and this modifications given as FCMQGPDL and DPCQGPDL, respectively
Summary
Image Segmentation is a key component of various computer vision applications It aims to divide a given image into perceptual regions, in which pixels in each region belong to a same visual object with small feature variations. The process of superpixel segmentation is to divide a given image into small, compact and regular regions, that contain pixels with similar texture, spatial positions brightness, color, and so on [7]. The AC is defined as the process of dividing the image (I), which contains NX samples, into different Kmax clusters. This can be performed by maximizing the between-cluster variation simultaneous with minimizing the within-cluster variation [28].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
