Comparison of multi-scale directional feature extraction methods for image processing

Abstract

Almost all images that are presented in classification problems regardless of area of application, have directional information embedded into its texture. Although there are many algorithms developed to extract this information, there is no ‘golden’ method that works the best every image. In order to evaluate performance of these developed algorithms, we consider 7 different multi-scale directional feature extraction algorithms along with our own multi-scale directional filtering framework. We perform tests on several problems from diverse areas of application such as font/style recognition on English, Arabic, Farsi, Chinese, and Ottoman texts, grading of follicular lymphoma images, and stratum corneum thickness calculation. We present performance metrics such as k-fold cross validation accuracies and times to extract feature from one sample, and compare with the respective state of art on each problem. Our multi-resolution computationally efficient directional approach provides results on a par with the state of the art directional feature extraction methods.