Plant Image Segmentation and Annotation with Ontologies in BisQue.
The field of computer vision has experienced much progress in the last two decades. Image analysis of photography and video has moved out of computer science research labs and into a wide range of applications. One example of progress in image analysis concerns the segmentation of images on the basis of gray scale, color hue, texture, geometry, and other features. Such image segmentation allows for increasingly refined classification of images and their components. In a parallel development, semantic computing has pursued the creation of ontologies in hopes of capturing and defining what it is we “know” about the world, and presenting it in the form of a terminology network connected by defined relationships. This knowledge network is computable, and makes it possible to make logical inferences about facts and data annotated with ontology terms. By combining these two innovations: image analysis and ontology annotation, we can imbue images with structured meaning and enable the inferential computability of image data. For example, it may be possible to segment an image of a plant leaf into diseased and undiseased tissue, and then to annotate these segments with ontology terms describing the disease state and associated phenotypes. Once a database of such images is developed, machine-learning algorithms can be applied to the data and predictive models can be developed. In this scenario, new images of plant leaves may be “tagged” with a disease state based on earlier examples. We have already explored the segmentation and ontological annotation components in the desktop application AISO , but would like to see this functionality available in an online format that allows for better processing scalability, storage, security, and collaborative feature sharing. We also want to apply machine learning to a collection of segmented, annotated images, thereby automating future image processing.