Discriminative Light Unsupervised Learning Network for Image Representation and Classification

This paper proposes a discriminative light unsupervised learning network (DLUN) to counter the image classification challenge. Compared with the traditional convolutional networks learning filters by the time-consuming stochastic gradient descent, DLUN learns the filter bank from diverse image patches with the classical K-means, which significantly reduces the training complexity while maintains the high discriminative ability. Besides, we design a new pooling strategy named voting pooling which considers the contribution difference of the adjacent activations. In the output layer, DLUN computes histograms in the size-changed dense sliding windows, followed by a max pooling operation on histogram bins at different scales to obtain the most competitive features. The classification performance on two widely used benchmarks verifies that DLUN is competitive among some state-of-the-arts.