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Erschienen in:
2022 | OriginalPaper | Buchkapitel
Image Segmentation Approach Based on Hybridization Between K-Means and Mask R-CNN
verfasst von : Hanae Moussaoui, Mohamed Benslimane, Nabil El Akkad
Erschienen in: WITS 2020
Verlag: Springer Singapore
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Abstract
In this article, we will introduce a hybrid method based on the combination of two image segmentation techniques. The first method adopted is the k-means algorithm which is an unsupervised machine learning technique used to group data points, the second is the Mask R-CNN which is a neural network architecture which combines two sub-problems: object detection and semantic segmentation. The main objective of this study is to approve the segmentation of the image using k-means. The first step is to apply Mask R-CNN on our original image to detect the objects that are present in the image, then, we will apply k-means clustering to have better segmentation. For our approach we used a set of metrics to evaluate our proposed approach such as the mean square error (MSE); the peak signal to noise ratio (PSNR) and many other measures such as the difference between two images. These measurements have shown satisfactory results and a performance of the proposed method.