Image Retrieval from CIFAR-10 Dataset - Project README

Overview

This project focuses on implementing image retrieval from the CIFAR-10 dataset using various machine learning classifiers. The goal is to develop models capable of accurately retrieving similar images from the dataset given a query image. The classifiers used in this project include K-Nearest Neighbors (KNN), K-Means Clustering, Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN).

About CIFAR-10 Dataset

The CIFAR-10 dataset is a widely used benchmark dataset in the field of computer vision. It consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. The dataset is commonly used for image classification tasks, but in this project, we focus on image retrieval.

The 10 classes in the CIFAR-10 dataset are:

1. Airplane
2. Automobile
3. Bird
4. Cat
5. Deer
6. Dog
7. Frog
8. Horse
9. Ship
10. Truck

Classifiers Used

1. K-Nearest Neighbors (KNN): A non-parametric method used for classification and regression. It assigns the label of the majority of the k nearest neighbors to the query point.
2. K-Means Clustering: An unsupervised clustering algorithm that partitions the dataset into k clusters based on similarity.
3. Linear Discriminant Analysis (LDA): A dimensionality reduction technique that finds the linear combinations of features that best separate different classes.
4. Principal Component Analysis (PCA): A dimensionality reduction technique that transforms the data into a new coordinate system to capture the maximum variance.
5. Support Vector Machines (SVM): A supervised learning algorithm that finds the hyperplane that best separates different classes in feature space.
6. Artificial Neural Networks (ANN): A class of machine learning algorithms inspired by the biological neural networks of the human brain.
7. Convolutional Neural Networks (CNN): A class of deep neural networks commonly used for analyzing visual imagery.

Project Structure

• models/: Contains scripts or notebooks for training and evaluating the classifiers.
• README.md: Overview of the project and instructions for setup and usage.

Getting Started

To get started with this project, follow these steps:

1. Clone the repository to your local machine.
2. Install the required dependencies (e.g., scikit-learn, TensorFlow, PyTorch).
3. Download the CIFAR-10 dataset and place it in the data/ directory.
4. Explore the provided scripts or notebooks for training and evaluating the classifiers.

Usage

• Use the provided scripts or notebooks to train, evaluate, and visualize the performance of different classifiers.
• Experiment with different hyperparameters, feature extraction techniques, and model architectures to improve retrieval accuracy.
• Share your findings and insights with the community.

Contributors

• Rishi Patel
• Ansh Mehta
• Dhruva Kumar Kaushal
• Saurav Soni
• Mansi Choudhary
• Keshika Sharma
• Karan Ganeshwala

Acknowledgments

• CIFAR-10 Dataset
• Scikit-learn Documentation
• TensorFlow Documentation