Haar Wavelet-Based Classification Method for Visual Information Processing Systems

Abstract

Nowadays, the systems for visual information processing are significantly extending their application field. Moreover, an unsolved problem for such systems is that the classification procedure has often-conflicting requirements for performance and classification reliability. Therefore, the goal of the article is to develop the wavelet method for classifying the systems for visual information processing by evaluating the performance and informativeness of the adopted classification solutions. This method of classification uses the Haar wavelet functions with training and calculates the ranges of changes in the coefficients of the separating surfaces. The authors proposed to select the ranges of changes in these coefficients by employing the Shannon entropy formula for measuring the information content. A case study proved that such a method will significantly increase the speed of detecting the intervals of coefficient values. In addition, this enables us to justify the choice of the width of the ranges for the change of coefficients, solving the contradiction between the performance and reliability of the classifier.