Image Classification using VLAD in MATLAB

This project implements an image classification pipeline using Vector of Locally Aggregated Descriptors (VLAD). The pipeline involves extracting Dense SIFT features, forming a dictionary, encoding features using VLAD, and training an SVM classifier with various hyperparameter optimizations. The goal is to achieve high accuracy in image classification by leveraging the VLAD encoding scheme.

Enlarged view of a Voronoi cell with local descriptors and the cell centre. Dotted lines depict the residuals between the local descriptors and the respective center.

Project Structure

• VLAD.m: Main script to run the project, including data loading, feature extraction, dictionary formation, VLAD encoding, and classification.
• denseSIFTNV.m: Extracts Dense SIFT features from the dataset.
• DictionaryFormationNV.m: Forms a dictionary using the extracted features.
• VLADNV.m: Encodes features using VLAD.
• splitTheDatastore.m: Splits the image datastore into training and testing sets.

How to Run

To run this project:

1. Ensure MATLAB is installed on your system.
2. Clone this repository to your local machine.
3. Place your dataset in a directory of your choice.
4. Open MATLAB and navigate to the cloned project directory.
5. Run the VLAD.m script to start the image classification pipeline.

run('VLAD.m')

License

This code is for teaching/research purposes only.