Image classification of coral reef components from underwater color video

The purpose of this study is to automate coral reef assessment, that is, to classify coral images into benthic categories from digitized underwater video using a computer-based classifier such that coral reef analysis becomes less subjective, less tedious and more precise. Corals exhibit a variety of color, texture and structure which are the visual cues used by marine scientists for their classification. In computer vision, color is a point property of a picture element while texture is a property of an area. Color and texture have been combined as color-texture which is a feature that describes the spatial organization of colors in an area. As inputs to a classifier, the authors extract color, texture and color-texture descriptors from coral images and measure recognition rates using each feature. Corals are 3D structures and, when imaged, are prone to varying resolutions, perspective projection and lighting conditions. Therefore, an additional objective of this study is to address the problem of illumination, rotation and scale invariance in pattern recognition of underwater images. Images were classified into one of five benthic categories: alive coral, dead coral, dead coral with algae, algae and abiotics. Overall, texture was found to be more discriminating than using color alone or color and texture combined. Dead coral was the most successfully recognized class using color features