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Q.: Active image labeling and its application to facial action labeling (2008)
| Venue: | ECCV 2008, Part II. LNCS |
| Citations: | 5 - 0 self |
BibTeX
@INPROCEEDINGS{Zhang08q.:active,
author = {Lei Zhang and Yan Tong and Qiang Ji},
title = {Q.: Active image labeling and its application to facial action labeling},
booktitle = {ECCV 2008, Part II. LNCS},
year = {2008},
pages = {706--719},
publisher = {Springer}
}
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Abstract
Abstract. For many tasks in computer vision, it is very important to produce the groundtruth data. At present, this is mostly done manually. Manual data labeling is labor-intensive and prone to the human errors. The training data it produces often lacks in both quantity and quality. Fully automatic data labeling, on the other hand, is not feasible and reliable. In this paper, we propose an interactive image labeling technique for efficient and accurate data labeling. The proposed technique includes two parts: an automatic labeling part and a human intervention part. Constructed on a Bayesian Network, the automatic image labeler produces an initial labeling of the image. A person then examines the initial labeling and makes some minor corrections. The selected human corrections and the image measurements are then integrated by the Bayesian Network framework to produce a refined labeling. To minimize the human involvement, an active user feedback strategy is developed, through which the optimal user feedback is determined, so that the labeling errors in the subsequent re-labeling process can be maximally reduced. The proposed framework combines the advantages of the human input with those of the machine so that the reliable, accurate, and efficient data labeling can be achieved. We demonstrate the validity of the proposed framework for interactive labeling of facial action units. The proposed methodology, however, is not limited to labeling of facial action units. It can be easily extended to other areas such as interactive image segmentation. 1
Keyphrases
facial action active image labeling initial labeling facial action unit interactive labeling interactive image labeling technique manual data labeling minor correction human intervention part interactive image segmentation automatic image labeler human correction active user feedback strategy proposed methodology computer vision human involvement training data optimal user feedback accurate data labeling human input human error fully automatic data labeling many task automatic labeling part bayesian network framework bayesian network subsequent re-labeling process image measurement efficient data labeling refined labeling groundtruth data labeling error
