A Review of Convolutional Neural Networks and Gabor Filters in Object Recognition

Convolutional neural networks (CNNs) have become a classic approach to solving challenging computer vision problems. Much of its success is due to its ability to discover optimal filters that capture non-trivial spatial relationships in data. Other vital components include advances in optimization, regularization, and overfitting prevention strategies. However, recently, researchers have observed closely the connections between what CNNs learn in the layers that capture low-level features and filter-banks such as Gabor filters. Gabor filters have been used in computer vision tasks long before CNNs were popularized with good performance. This paper presents a review of the literature concerning approaches that involve both Gabor filters and CNNs. We pay close attention to successes and opportunities for future research in the intersection of these two computer vision tools.