A multilevel threshold image segmentation method based on hybrid Particle Swarm Optimization(PSO) and Gravitation Search Algorithm(GSA) was proposed to solve the weakness that a single algorithm in image segmentation has a lower local searching ability.A strategy of generalized opposition-based learning in image segmentation was proposed to improve the population diversity and to strengthen the global searching ability in optimizing processing.The normal mutation strategy on the best particle was conducted to extend the searching space and to avoid the premature convergence of the algorithm.Then,the multilevel threshold image segmentation method of hybrid PSOGSA with generalized opposition-based learning was implemented.Finally,complex image segmentation experiments were processed by proposed method and the results were compared with those of multilevel threshold segmentation methods of GSA and Firefly Algorithm(FA).Experimental results show the proposed method possesses a higher accuracy in multilevel threshold segmentation and the standard deviation of best objective values in continuous operation has decreased by up to 90%.Therefore,the image segmentation method of multilevel threshold using the hybrid PSOGSA with generalized opposition-based learning can be accurately and stably used in multilevel threshold image segmentation.