Unsupervised Deep Feature Transfer for Low Resolution Image Classification

Yuanwei Wu, Ziming Zhang, Guanghui Wang

Abstract:

In this paper, we propose a simple while effective unsupervised deep feature transfer algorithm for low resolution
image classification. No fine-tuning on convenet filters is
required in our method. We use pre-trained convenet to
extract features for both high- and low-resolution images,
and then feed them into a two-layer feature transfer network
for knowledge transfer. A SVM classifier is learned directly
using these transferred low resolution features. Our network can be embedded into the state-of-the-art deep neural
networks as a plug-in feature enhancement module. It preserves data structures in feature space for high resolution
images, and transfers the distinguishing features from a wellstructured source domain (high resolution features space) to
a not well-organized target domain (low resolution features
space). Extensive experiments on VOC2007 test set show
that the proposed method achieves significant improvements
over the baseline of using feature extraction.