Content-Based Feature Extraction for Image Recognition - Shape Acquisition via K-means Clustering Segmentation

The content-based feature extraction phase for image recognition primarily involves the process of k-means clustering segmentation and shape information acquisition. In this stage, we implement the k-means clustering algorithm to partition the image, dividing different regions into distinct clusters. The algorithm works by iteratively assigning pixels to the nearest cluster center based on color or intensity values and updating centroids until convergence. Following segmentation, we extract shape characteristics and other relevant information from each cluster using morphological operations and contour detection methods. These extracted features may include boundary descriptors, area measurements, and geometric properties that serve as inputs for subsequent image recognition tasks. Through these systematic steps, we can effectively derive crucial shape-based features from images, thereby enabling accurate content-based image recognition. The implementation typically involves OpenCV functions like kmeans() for clustering and findContours() for shape analysis.