kirovd/imageclustering

Image Clustering Project

Overview

This project implements an image clustering algorithm using machine learning techniques. It is designed to categorize pixels or groups of pixels in an image into distinct clusters based on their similarities.

Key Features

• Pixel Clustering: Classify pixels in an image into distinct clusters.
• Pattern Recognition: Algorithm identifies patterns or features common to pixel groups.
• Image Segmentation: Results in a segmented image, with each cluster representing a grouping of pixels sharing similar characteristics such as color or intensity.

Objective

The primary objective of this project is to explore the capabilities of AI in image processing, particularly in simplifying complex images into distinct clusters. This serves as a fundamental study in the field of computer vision and image analysis, laying the groundwork for further advancements and applications in various domains such as medical imaging, remote sensing, and digital media.

How It Works

The image clustering algorithm operates by analyzing an input image and processing it using a model like K-means. The algorithm identifies and groups pixels or pixel clusters based on common features, such as color or intensity. This process simplifies the image into distinct, visually identifiable regions.

Technologies Used

• Python: The primary programming language for the project.
• NumPy: For efficient numerical computing.
• Scikit-learn (sklearn.cluster.KMeans): For implementing the K-means clustering algorithm.
• Scikit-image (skimage.io): For image processing tasks.
• Matplotlib: For plotting and visualizing data.