VISUAL IMAGE ANNOTATION UTILIZING MACHINE LEARNING FOR IN-TIME FEEDBACK

An interactive learning cycle includes an operator, a computer and a pool of images. The operator produces a sparsely-labeled data set. A back-end system produces live feedback: a densely-labeled training set which is displayed on the computer. Immediate feedback is displayed in color on the operator computer in less than about five seconds. A labeling tool displays a user interface and for every labeling project a region is defined that is downloaded as an image data batch. The operator annotates on a per-image basis in the region and uses several UI tools to mark features in the image and group them to a predefined label class. The back-end system includes processes that run in parallel and feed back into each other, each executing a model. A local model is used independently of the global model. The global model accepts sparsely-labeled images from numerous operator computers.