MATLAB Implementation of K-Nearest Neighbors (KNN) Classifier

In digital image processing and analysis, the K-Nearest Neighbors (KNN) classifier serves as a fundamental classification method. This approach enables the determination of unknown sample categories by comparing them with labeled training samples. The classifier employs distance metrics (typically Euclidean distance for image feature vectors) to quantify similarity between unknown samples and known data points. During classification, the algorithm identifies the k closest training samples (neighbors) based on calculated distances. The unknown sample is then assigned to the most frequent class among these k neighbors through majority voting. While KNN represents a straightforward and intuitive classification technique, it remains particularly valuable in digital image processing for tasks like pattern recognition and pixel classification. MATLAB implementation typically involves functions like pdist2 for distance computation and mode for determining the predominant class among neighbors.