Improved Otsu Theory of Image Multi-threshold Segmentation by Incorporating Ant Colony Algorithm

Abstract

In today's information age, people have higher requirements for image processing and classification, and are increasingly concerned about the topic of multi-threshold image segmentation. In recent years, with the continuous development of computer technology and theoretical basis, the relevant research direction gradually towards more accurate and efficient direction. It is an algorithm that has both global and localised characteristics and is able to deal with dynamic changes in uncertain environments. This paper analyses the improved multi-threshold image segmentation by introducing the improved OTSU theory, which is based on the information entropy and greedy factor and other related algorithms to segment the image, and can achieve two goals under this model: one is to maximise the grey value; the other is the optimal path. The author firstly analyzes the foraging routes of ants and other experimental data and finds that there is not much discrepancy. Secondly, he finds a new method to reduce the competition between ant colonies and also reduce the noise pollution level, thus improving the overall performance and convergence speed.