Acta Scientific Computer Sciences

Research Article Volume 4 Issue 1

Cascaded Semi-supervised Annotation Tool for Image Data Labeling

Ruixu Liu* and Vijayan K Asari

Department of Electrical and Computer Engineering, University of Dayton, USA

*Corresponding Author: Ruixu Liu, Department of Electrical and Computer Engineering, University of Dayton, USA.

Received: November 15, 2021; Published: December 13, 2021

Abstract

Object detection algorithms have advanced rapidly during the last decade, especially after realizing the efficiency of Convolutional Neural Networks (CNN) for feature extraction. However, it is a time-consuming and labor-intensive task to annotate the objects’ bounding boxes as ground truth data that is necessary to efficiently train the CNN for object detection and recognition tasks. We propose a semi-supervised cascaded bounding box labeling strategy for fast and efficient data annotation for large-scale ground-truth generation. We observed around 80% reduction of workload in data annotation by employing our semi-supervised ground-truth generation method on MS COCO dataset.

Keywords: Object Detection; Bounding Box Labeling; Semi-supervised Method; Data Annotation; Ground-truth Generation

References

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Citation

Citation: Ruixu Liu and Vijayan K Asari. “Cascaded Semi-supervised Annotation Tool for Image Data Labeling". Acta Scientific Computer Sciences 4.1 (2022): 10-15.

Copyright

Copyright: © 2022 Ruixu Liu and Vijayan K Asari. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




Metrics

Acceptance rate35%
Acceptance to publication20-30 days

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