FOREST: A flexible object recognition system

Abstract

Despite the growing importance of image data, image recognition has succeeded in taking a permanent role in everyday life in specific areas only. The reason is the complexity of currently available software and the difficulty in developing image recognition systems. Currently available software frameworks expect users to have a comparatively high level of programming and computer vision skills. FOREST - a flexible object recognition framework - strives to overcome this drawback. It was developed for non-expert users with little-to-no knowledge in computer vision and programming. While other image recognition systems focus solely on the recognition functionality, FOREST covers all steps of the development process, including selection of training data, ground truth annotation, investigation of classification results and of possible skews in the training data. The software is highly flexible and performs the computer vision functionality autonomously by applying several feature detection and extraction operators in order to capture important image properties. Despite the use of weakly supervised learning, applications developed with FOREST achieve recognition rates between 86 and 99% and are comparable to state-of-the-art recognition systems.