Very deep recurrent convolutional neural network for object recognition

17 March 2017 Very deep recurrent convolutional neural network for object recognition

Sourour Brahimi, Najib Ben Aoun, Chokri Ben Amar

Author Affiliations +

Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 1034107 (2017) https://doi.org/10.1117/12.2268672
Event: Ninth International Conference on Machine Vision, 2016, Nice, France

Abstract

In recent years, Computer vision has become a very active field. This field includes methods for processing, analyzing, and understanding images. The most challenging problems in computer vision are image classification and object recognition. This paper presents a new approach for object recognition task. This approach exploits the success of the Very Deep Convolutional Neural Network for object recognition. In fact, it improves the convolutional layers by adding recurrent connections. This proposed approach was evaluated on two object recognition benchmarks: Pascal VOC 2007 and CIFAR-10. The experimental results prove the efficiency of our method in comparison with the state of the art methods.

Citation Download Citation

Sourour Brahimi, Najib Ben Aoun, and Chokri Ben Amar "Very deep recurrent convolutional neural network for object recognition", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 1034107 (17 March 2017); https://doi.org/10.1117/12.2268672

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE