Clustering-based Image Segmentation Techniques
Image segmentation using clustering is a machine learning technique that divides an image into distinct regions or objects based on the similarity of their pixel values. This can be useful for a variety of tasks, such as object detection, scene understanding, and image editing.
Image segmentation using clustering is a process of dividing an image into distinct regions or objects based on the similarity of their pixel values in the feature space. In this technique, clustering algorithms such as k-means, fuzzy c-means, and hierarchical clustering are used to group the pixels into clusters based on their similarity in color, texture, or other relevant features. Once the clusters have been formed, each pixel in the image is assigned to the cluster that it is most similar to.
The result is a segmented image with distinct regions or objects that can be further analyzed or processed. Image segmentation using clustering is a widely used technique in computer vision and image processing applications such as object recognition, scene understanding, and image editing