We present a real-time interactive image segmentation system for the semantic analysis of images. It is a feasible solution to a variety of real quality control tasks mainly because it adopts a novel interactive training and classification mode. In general, our system incorporates the interactive acquisition of training data into the automatic classification based on adaptive boosting.
To reduce the manual effort required for interactively acquiring the online training data, the input images are efficiently processed through GPU-based over-segmentation. We also developed a user-friendly graphical user interface to allow users to edit (select, deselect and reselect) the training data. It also allows the users to add more training data to improve the classifier if necessary.