Sample Selection for Efficient Image Annotation

Bishwo Adhikari, Esa Rahtu, Heikki Huttunen

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

2 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
OtsikkoProceedings of the 2021 9th European Workshop on Visual Information Processing, EUVIP 2021
ToimittajatA. Beghdadi, F. Alaya Cheikh, J.M.R.S. Tavares, A. Mokraoui, G. Valenzise, L. Oudre, M.A. Qureshi
KustantajaIEEE
Sivumäärä6
ISBN (elektroninen)9781665432306
ISBN (painettu)9781665432313
DOI - pysyväislinkit
TilaJulkaistu - 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaEuropean Workshop on Visual Information Processing - Paris, Ranska
Kesto: 23 kesäk. 202125 kesäk. 2021

Julkaisusarja

NimiEuropean Workshop on Visual Information Processing
ISSN (painettu)2164-974X
ISSN (elektroninen)2471-8963

Conference

ConferenceEuropean Workshop on Visual Information Processing
Maa/AlueRanska
KaupunkiParis
Ajanjakso23/06/2125/06/21

Julkaisufoorumi-taso

  • Jufo-taso 1

!!ASJC Scopus subject areas

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

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