Deep FisherNet for Object Classification

Understand

Despite the great success of convolutional neural networks (CNN) for the image classification task on datasets like Cifar and ImageNet, CNN's representation power is still somewhat limited in dealing with object images that have large variation in size and clutter, where Fisher Vector (FV) has shown to be an effective encoding strategy.

• FV encodes an image by aggregating local descriptors with a universal generative Gaussian Mixture Model (GMM).
• FV however has limited learning capability and its parameters are mostly fixed after constructing the codebook.
• To combine together the best of the two worlds, we propose in this paper a neural network structure with FV layer being part of an end-to-end trainable system that is differentiable; we name our network FisherNet that is learnable using backpropagation.