README.md

Comparative Evaluation of Feature Descriptors Through Bag of Visual Features with Support Vector Machine on Embedded GPU System

Overview

Comparative evaluation of Local Feature Descriptors such as SIFT, SURF, and KAZE, and Local Binary Descriptors such as BRIEF, ORB, BRISK, AKAZE, and FREAK through Bag of Visual Features (BoVF) approach with Support Vector Machine (SVM) classifier for the tasks of recognition and classification on six visual datasets (MNIST, JAFFE, Extended CK+, FEI, CIFAR-10, and FER-2013) on an embedded GPU system: The NVIDIA's Jetson Nano.

Notice:

• The proposed work was implemented in Python (version 3.6) as well as OpenCV (version 4.1) using a GPU ARM architecture and might not work with other versions.

• In this repository, only the directory with the MNIST, CIFAR-10, and FER-2013 visual datasets are available. The directory where the other visual datasets should stay is not available in this GitHub repository since it would violate the dataset rules.

• To download and use other visual datasets presented here, we strongly recommend reading the use of the norms of them. However, we recommend reading the use of the norms of the CIFAR-10, FER-2013, and MNIST as well.

Dependencies

To install the dependencies run:

pip install -r requirements.txt

Usage

python main.py --detector <detector> --descriptor <descriptor> --dataset <dataset>

Arguments	Info
-h, --help	Show help message and exit
--detector	Specify SIFT or SURF or KAZE or ORB or BRISK or AKAZE
--descriptor	Specify SIFT or SURF or KAZE or BRIEF or ORB or BRISK or AKAZE or FREAK
--dataset	Specify MNIST or JAFFE or Extended-CK+ or FEI or CIFAR-10 or FER-2013

Examples

Help

python main.py --help

Evaluation of BRIEF Descriptor on CIFAR-10 visual datasets

python main.py --detector ORB --descriptor BRIEF --dataset CIFAR-10

Credits

• Mlubega

• Fast.ai

• Scikit-learn.org

License

Code released under the MIT license.