Second-order Convolutional Neural Networks

Understand

Convolutional Neural Networks (CNNs) have been successfully applied to many computer vision tasks, such as image classification.

• By performing linear combinations and element-wise nonlinear operations, these networks can be thought of as extracting solely first-order information from an input image.
• In the past, however, second-order statistics computed from handcrafted features, e.g., covariances, have proven highly effective in diverse recognition tasks.
• In this paper, we introduce a novel class of CNNs that exploit second-order statistics.