Texture analysis using income inequality metrics

Texture analysis on digital images can be used to correctly segment areas of an image and/or classify different objects within the field of view of a digital camera. Depending on the application, one can use a Gray Level Co-occurrence Matrix (GLCM) to detect patterns or a simple contrast measure such as variance can be a good metric as well. In this work, our main objective is to define a set of new texture metrics simple enough to be computationally efficient so that fast processing is feasible. Thus in our previous two texture techniques mentioned before, GLCM and variance, metrics such as variance would be selected for comparison purposes as they require less computational time. Thinking outside the box, we would like to introduce the use of income inequality metrics used in the field of economy to measure the distribution of income and wealth inequality within a population. We found that these metrics can be used on texture analysis of digital images.