Colour Texture Analysis

Abstract

This chapter presents a novel and generic framework for image segmentation using a compound image descriptor that encompasses both colour and texture information in an adaptive fashion. The developed image segmentation method extracts the texture information using low-level image descriptors (such as the Local Binary Patterns (LBP)) and colour information by using colour space partitioning. The main advantage of this approach is the analysis of the textured images at a micro-level using the local distribution of the LBP values, and in the colour domain by analysing the local colour distribution obtained after colour segmentation. The use of the colour and texture information separately has proven to be inappropriate for natural images as they are generally heterogeneous with respect to colour and texture characteristics. Thus, the main problem is to use the colour and texture information in a joint descriptor that can adapt to the local properties of the image under analysis. We will review existing approaches to colour and texture analysis as well as illustrating how our approach can be successfully applied to a range of applications including the segmentation of natural images, medical imaging and product inspection.

Identifying Medicinal Plant Leaves using Textures and Optimal Colour Spaces Channel

“…There is no particular colour space that is proving itself best for all the colour images. The use of colour increases the performances of the standard grey level texture analysis techniques [12,13]. To exploit the advantage of the colour component of the medicinal plant leaves which are mostly green in colour, identifying medicinal plant leaves using colour shall be considered in this research work.…”