Deriving dimensionless features for color object recognition in different color models

摘要

Pattern recognition is an important aspect of image processing. Image features are computed from image objects and subsequently used by an object classifier to map (and therefore classify) image objects into their corresponding object classes. To avoid misclassification the image features used should be selected in such a way that they represent the image object similarity appropriately. Similarity however is a well known theoretical concept in physics, where similar phenomena are mathematically expressed as constant dimensionless numbers. These dimensionless numbers are determined from the dimensional representation of the relevant variables by means of a technique called dimensional analysis. In consequence, the concept of dimensional analysis is applied to the derivation of dimensionless features of color images based on various color models. The properties such as color constancy of the resulting dimensionless numbers are studied using analytical and numerical examples. Also the resulting similarity from the different color models is analyzed and discussed.