Object Detection and Classification: A Joint Selection and Fusion Strategy of Deep Convolutional Neural Network and SIFT Point Features
Published in Multimedia Tools and Applications (Springer), 2018
Authors:
Muhammad Rashid, Muhammad Attique Khan, Muhammad Sharif, Mudassar Raza, Muhammad Masood, Farhat Afza
This work presents a hybrid framework for object detection and classification by combining deep convolutional neural network (CNN) features with SIFT-based handcrafted features. The proposed approach performs feature selection and fusion to improve classification performance by leveraging both deep and traditional feature representations.
Contributions 📃
- A hybrid framework combining CNN features and SIFT features.
- Joint feature selection and fusion strategy for improved performance.
- Enhanced object detection and classification accuracy.
- Demonstration of the effectiveness of combining deep and handcrafted features.
📖 Citation (BibTeX)
@article{rashid2018object,
title = {Object Detection and Classification: A Joint Selection and Fusion Strategy of Deep Convolutional Neural Network and SIFT Point Features},
author = {Rashid, Muhammad and Khan, Muhammad Attique and Sharif, Muhammad and Raza, Mudassar and Masood, Muhammad and Afza, Farhat},
journal = {Multimedia Tools and Applications},
year = {2018},
publisher = {Springer},
doi = {10.1007/s11042-018-7031-0}
}