Authors:
Julia Moehrmann
and
Gunther Heidemann
Affiliation:
University of Osnabrück, Germany
Keyword(s):
Image Recognition System, Development, Image Recognition, Image Annotation, Ground Truth Annotation.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image Understanding
;
Object Recognition
;
Pattern Recognition
;
Software Engineering
;
Web Applications
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 detectio
n 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.
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