RIFD CNN: Rotation Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection
Thanks to the powerful feature representations obtained through deep convolutional neural network (CNN) the performance of object detection has recently been substantially boosted. Despite the remarkable success, the problems of object rotation, within class variability, and between class similarity remain several major challenges. To address these problems, this paper proposes a novel and effective method to learn a rotation invariant and Fisher discriminative CNN (RIFD CNN) model. This is achieved by introducing and learning a rotation invariant layer and a Fisher discriminative layer, respectively, on the basis of the existing high capacity CNN architectures. Specifically, the rotation invariant layer is trained by imposing an explicit regularization constraint on the objective function that enforces invariance on the CNN features before and after rotating. The Fisher discriminative layer is trained by imposing the Fisher discrimination criterion on the CNN features so that they have small within class scatter but large between class separation. In the experiments, we comprehensively evaluate the proposed method for object detection task on a public available aerial image dataset and the PASCAL VOC 2007 dataset. State of the art results are achieved compared with the existing baseline methods.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)