A Novel Color Texture Classification using Sparse Coding based on Quaternionic Representation

Texture and color are two important attributes for object recognition. Recently, quaternionic representation of color images have been used as an effective method for color image processing. Using such a representation, it is possible to consider the mutual interaction between different color channels. In the last decade, several quaternion operations like rotation, reflection, and Clifford translation have been developed. Such operators are able to extract shallow information from the color images. In this paper, we first propose a set of new quaternion operators called hybrid quaternionic operators, which can be produced by a cascade of several simple quaternionic operators. Such operators can extract deeper information from the color images. We then use such operators, and present a novel color texture classification method using the concept of sparse coding. Experimental results indicate that the proposed method outperforms several existing and popular methods.