Prototype based relative attribute learning

According to the research on representation learning, a proper feature representation of data has a greater impact than classifiers on classification. It’s almost become the most important part in system design. In this paper, based on prototype theorem in psychology, a new feature is proposed. Specifically, the prototype dataset is composed of representative data of extra datasets. Then, the rank functions are derived based on the relationship between the prototype dataset and any data set. Thus, any data could be represented via the rank functions and the values of the functions are their new features. The proposed method has been checked on the MINST database and Pubfig database. Compared with the gray-scale feature and attribute, the prototype based relative attribute is more reasonable and has better performance.