Sample selection for efficient image annotation
This work addresses the costly and time-consuming process of acquiring labeled data for object detection, offering a practical solution for researchers and practitioners in computer vision.
The paper tackles the problem of reducing manual annotation workload for supervised object detection by proposing an efficient image selection approach that samples the most informative images from an unlabeled dataset, achieving up to an 80% reduction in annotation effort compared to full manual labeling.
Supervised object detection has been proven to be successful in many benchmark datasets achieving human-level performances. However, acquiring a large amount of labeled image samples for supervised detection training is tedious, time-consuming, and costly. In this paper, we propose an efficient image selection approach that samples the most informative images from the unlabeled dataset and utilizes human-machine collaboration in an iterative train-annotate loop. Image features are extracted by the CNN network followed by the similarity score calculation, Euclidean distance. Unlabeled images are then sampled into different approaches based on the similarity score. The proposed approach is straightforward, simple and sampling takes place prior to the network training. Experiments on datasets show that our method can reduce up to 80% of manual annotation workload, compared to full manual labeling setting, and performs better than random sampling.