Deep feature extraction in the DCT domain

Abstract

We explore the effectiveness of deep features extracted by Convolutional Neural Networks(CNNs) in the Discrete Cosine Transform(DCT) domain for various image classification tasks such as pedestrian and face detection, material identification and object recognition. We perform the DCT operation on the feature maps generated by convolutional layers in CNNs. We compare the performance of the same network on the same datasets, with the same hyper-parameters with or without the DCT step. Our results indicate that a DCT operation incorporated into the network after convolution+thresholding and before pooling can have certain advantages such as convergence over fewer training epochs and sparser weight matrices that are more conducive to pruning and hashing techniques.