This paper suggests a solution for the image segmentation (IS) problem with the multilevel thresholding based on one of the latest hybrid swarm computation optimization algorithms, particle swarms, and gravitational search (PSGA). The experimental results are comparable with other state-of-the-art algorithms that show that the PSGA on selected images is better than the competitors. | An Optimizing Multilevel Thresholding for Image Segmentation Based on Hybrid Swarm Computation Optimization Thi-Kien Dao 1 Hong-Jiang Wang1 Jie Yu2 Huu-Quynh Nguyen3 Truong-Giang Ngo3 B and Trong-The Nguyen4 1 Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou 350118 China 2 College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou 350118 China 3 Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Vietnam giangnt@ 4 Department of Information Technology Haiphong University of Management and Technology Haiphong Vietnam Abstract. This paper suggests a solution for the image segmentation IS problem with the multilevel thresholding based on one of the latest hybrid swarm compu- tation optimization algorithms particle swarms and gravitational search PSGA . The experimental results are comparable with other state-of-the-art algorithms that show that the PSGA on selected images is better than the competitors. Keywords Cross-entropy thresholding Image segmentation Particle warms And gravitational search 1 Introduction Image threshold segmentation is one of the most effective and real-time methods that have received widespread attention in image processing 1 . Multi-threshold image seg- mentation is considered as an extension of threshold segmentation that can distinguish background and multiple goals but the disadvantage is that the calculation is compli- cated and takes a long consumption time. Many biological heuristics is the promising ways of applying successfully to deal with IS problems . genetic evolution swarm behavior 2 . For example the gravity search algorithm GSA 3 was taken inspiration based on the theory of Newtonian physics as the gravity law and mass interactions the FA algorithm was taken inspiration from Firefly insect 4 the CS algorithm was mim- icked from Cuckoo search 5 . Some applications in image processing as .
