Unsupervised learning of quaternion features for image classification

Abstract

Unsupervised feature learning is a very popular trend in image classification. Most of the methods for unsupervised feature learning produces filters which operate either on intensity or color information. In this paper we propose a quaternion-based approach for unsupervised feature learning which makes possible joint encoding of the intensity and color information. The image representation is computed using quaternion principal component analysis and k-means clustering. We experimentally show that our approach outperforms the existing approach for unsupervised feature learning from color images, achieving classification accuracy of 91% on a dataset of remote sensing images.