High speed deep networks based on Discrete Cosine Transformation

The traditional deep networks take raw pixels of data as input, and automatically learn features using unsupervised learning algorithms. In this configuration, in order to learn good features, the networks usually have multi-layer and many hidden units which lead to extremely high training time costs. As a widely used image compression algorithm, Discrete Cosine Transformation (DCT) is utilized to reduce image information redundancy because only a limited number of the DCT coefficients can preserve the most important image information. In this paper, it is proposed that a novel framework by combining DCT and deep networks for high speed object recognition system. The use of a small subset of DCT coefficients of data to feed into a 2-layer sparse auto-encoders instead of raw pixels. Because of the excellent decorrelation and energy compaction properties of DCT, this approach is proved experimentally not only efficient, but also it is a computationally attractive approach for processing high-resolution images in a deep architecture.