Environmental Microbiological Content-Based Image Retrieval System Using Internal Structure Histogram

Environmental Microbiology (EM) is an important scientific field, which investigates the ecological usage of different microorganisms. Traditionally, researchers look for the information of microorganisms by checking references or consulting experts. However, these methods are time-consuming and not effective. To increase the effectiveness of EM information search, we propose a novel approach to aid the information searching work using Content-based Image Retrieval (CBIR). First, we use an microorganism image as input data. Second, image segmentation technique is applied to obtain the shape of the microorganism. Third, we extract shape feature from the segmented shape to represent the microorganism. Especially, we use a contour-based shape feature called Internal Structure Histogram (ISH) to describe the shape, which can use angles defined on the shape contour to build up a histogram and represent the structure of the microorganism. Finally, we use Euclidean distances between each ISHs to measure the similarity of different EM images in the retrieval task, and use Average Precision (AP) to evaluate the retrieval result. The experimental result shows the effectiveness and potential of our EM-CBIR system.