In this project i have Implemented conventional k-means clustering algorithm for gray-scale image and colored image segmentation.
K-means algorithm is an unsupervised clustering algorithm that classifies the input data points into multiple classes based on their inherent distance from each other. The algorithm assumes that the data features form a vector space and tries to find natural clustering in them.
kmeans.py uses ideal output segmentation file (out1.jpg) is used to calculate/evaluate accuracy of the segmentation for the gray scale image. The code gives TP rate, FP rate, and F-score as outputs.
kmeans_color.py uses ideal output segmentation file (out2.jpg) is used to calculate/evaluate accuracy of the segmentation for the color image. The code gives TP rate, FP rate, and F-score as outputs.