Jumping particle swarm optimization algorithm framework for content-based image retrieval system

Content-based image retrieval (CBIR) has been studied well in the last decades in numerous research fields such as medicine, journalism, and private life. Applications of CBIR have been widely employed in medical images due to their direct impact on human life. With continues growing of digital libraries, there is a need for an efficient method to retrieve images from large datasets. In this paper, a new method was developed for CBIR based on the jumping particle swarm optimization (JPSO) algorithm. The proposed algorithm represents a developed instant of particle swarm optimization (PSO). However, JPSO the approach does not consider the velocity components to guide particle movements in the problem space. Instead of relying on inertia and velocity, intermittently random jumps (moves) occur from one solution to another within the discrete search space. To test the performance of the proposed algorithm, three types of medical image databases were used in the experiment which are the endoscopy 100, dental 100, and 50 skull image databases. The results show that the proposed algorithm could achieve high accuracy in image extraction and retrieve the accurate image category compared with other research works.