Optimizing the calculation of directional gradients for image recognition

Abstract

The possibilities of the optimization of histogram of oriented gradients calculations for solving image content recognition problems described based on the 48 × 48 pixels size image example. The algorithm doesn’t change in regards to the input data and suits for the histogram of the oriented gradients calculation based on any image. The algorithm idea is taken from the work of Soojin Kim and Kyeongsoon Cho [1], which is the modification of original HOG descriptor algorithm presented by Navneet Dalal and Bill Triggs [3] aimed at optimization of the calculation speed without loosing accuracy during image content recognition using HOG descriptor to generate the set of features of the image content. The algorithm is described in detail in the next sequence of actions. 1) Original HOG feature calculation. We use it as the first step since the algorithm is the optimized version of the original HOG feature calculation. 2) Solving the aliasing problem and accuracy improvement by using the interpolation technique during the HOG feature calculation process. We use one of the normalization schemes, applying interpolation as the next calculation step. 3) Solving the redundant operations and calculation speed problems by using cell-based operations and applying from one to four described types to the cells, based on which depends the detection window cell calculation. The types are applied to cell based on the blocks intersection containing the cell. The computer program has been developed according to the selected optimized algorithm of HOG feature calculation. It was used during the image content features description and learning process and in the further computer vision research. The software implementation of the algorithm takes into account the capabilities of modern computer technology, Javascript programming language and modern needs of the image content recognition calculation speed and accuracy. The implementation of computer program logic is shown in the script examples, utilizing modularity and parallel calculation as the strong sides of Javascript, further improving HOG feature calculation speed.