Sample Selection for Efficient Image Annotation

Bishwo Adhikari, Esa Rahtu, Heikki Huttunen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


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.

Original languageEnglish
Title of host publicationProceedings of the 2021 9th European Workshop on Visual Information Processing, EUVIP 2021
EditorsA. Beghdadi, F. Alaya Cheikh, J.M.R.S. Tavares, A. Mokraoui, G. Valenzise, L. Oudre, M.A. Qureshi
Number of pages6
ISBN (Electronic)9781665432306
ISBN (Print)9781665432313
Publication statusPublished - 2021
Publication typeA4 Article in a conference publication
EventEuropean Workshop on Visual Information Processing - Paris, France
Duration: 23 Jun 202125 Jun 2021

Publication series

NameEuropean Workshop on Visual Information Processing
ISSN (Print)2164-974X
ISSN (Electronic)2471-8963


ConferenceEuropean Workshop on Visual Information Processing


  • Bounding Box
  • Image Annotation
  • Object Detection
  • Sample Selection

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Information Systems
  • Signal Processing


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