Content-Based Microscopic Image Retrieval of Environmental Microorganisms Using Multiple Colour Channels Fusion

Environmental Microorganisms (EMs) are usually unicellular and cannot be seen with the naked eye. Though they are very small, they impact the entire biosphere by their omnipresence. Traditional DeoxyriboNucleic Acid (DNA) and manual investigation in EMs search are very expensive and time-consuming, we develop an EM search system based on Content-based Image Retrieval (CBIR) method by using multiple colour channels fusion. The system searches over a database to find EM images that are relevant to the query EM image. Through the CBIR method, the features are automatically extracted from EM images. We compute the similarity between a query image and EM database images in terms of each colour channel. As many colour channels exist, a weight fusion of similarity in different channels is required. We apply Particle Swarm Optimisation (PSO), Fish Swarm Optimisation Algorithm (FSOA), Invasive Weed Optimization (IWO) and Immunity Algorithm (IA) to laugh fusion. Then obtain the re-weighted EM similarity and final retrieval result. Experiments on our EM dataset show the advantage of the proposed multiple colour channels fusion method over each single channel result.