Sample selection for efficient image annotation

TL;DR

This paper introduces an efficient image selection method that reduces manual annotation effort by up to 80% in supervised object detection tasks through a similarity-based sampling approach prior to training.

Contribution

It proposes a novel image sampling technique using CNN features and similarity scores to select informative images, improving annotation efficiency in object detection.

Findings

Reduces manual annotation workload by up to 80%.

Outperforms random sampling in selecting informative images.

Effective prior to network training, streamlining the annotation process.

Abstract

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.