Centroid-based pattern recognition is a technique used in computer vision and image processing for feature extraction and classification. This repository contains Python code that implements a centroid-based pattern recognition system. The system extracts features from images based on the centroids of grid cells and uses a Decision Tree Classifier or Random Forest Classifier for image classification.
•Extracts features from images based on centroids of grid cells.
•Utilizes a Decision Tree Classifier or Random Forest Classifier for image classification.
•Allows for customization of grid dimensions.
This project aims to demonstrate the application of centroid-based feature extraction in pattern recognition tasks, particularly in image classification. By dividing the image into a grid and calculating the centroids of each grid cell, we can extract meaningful features for classification.
In initial experiments, a Decision Tree Classifier was used for classification. However, further experimentation revealed that utilizing a Random Forest Classifier yielded higher accuracy. This highlights the importance of exploring different classifiers and parameters to achieve optimal performance in pattern recognition tasks.
•This implementation is for educational purposes and may require customization for specific applications.
•Ensure that images used for classification are appropriately preprocessed and labeled.