Application of deep learning to computer vision: A comprehensive study
Deep learning is a new era of machine learning research, where many layers of information processing stages are exploited for unsupervised feature learning. Using multiple levels of representation and abstraction, it helps a machine to understand about data (e.g. images, sound and text) more accurately. Many deep learning models have been proposed for solving the problem of different applications. Therefore, a comprehensive knowledge of these models is demanded to select the appropriate one for a specific application areas in signal or data processing. This paper reviews several deep learning models proposed for different application area in the field of computer vision, and makes a comprehensive evaluation of two well known models namely AlexNet and VGG_S in nine different benchmark datasets. The experimental results show that these two models perform better than the existing state of the art deep learning models in one dataset.
2016 5th International Conference on Informatics, Electronics and Vision (ICIEV)