Image Segmentation via K-means Clustering Combined with Region Growing

This document presents an image segmentation method based on consistency extraction. The approach employs the K-means clustering algorithm for initial edge detection, which partitions image pixels into k clusters based on feature similarity using Euclidean distance calculations. Following clustering, region growing techniques are implemented to refine segmentation boundaries by iteratively merging adjacent pixels with similar properties. The algorithm typically involves seed point selection, neighbor evaluation using similarity thresholds, and region expansion until no more pixels meet the inclusion criteria. For deeper investigation into this methodology, readers can refer to the author's cited research papers containing detailed algorithmic implementations and performance evaluations.

Beyond this technique, the field of image segmentation offers numerous alternative approaches. Deep learning-based segmentation methods have demonstrated significant advancements, particularly through convolutional neural networks (CNNs) with architectures like U-Net and Mask R-CNN that automatically learn hierarchical features for pixel-wise classification. Additionally, similarity-based algorithms such as mean shift segmentation operate by identifying dense regions in feature space through kernel density estimation and mode seeking, effectively grouping pixels with comparable color and texture characteristics. Those interested in further exploration can consult relevant research publications or technical textbooks covering fundamental concepts, code implementations in frameworks like OpenCV or TensorFlow, and comparative analyses of segmentation performance metrics.