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Architecture to improve the accuracy of automatic image annotation systems

Khatchatoorian, A. G ; Sharif University of Technology | 2020

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  1. Type of Document: Article
  2. DOI: 10.1049/iet-cvi.2019.0500
  3. Publisher: Institution of Engineering and Technology , 2020
  4. Abstract:
  5. Automatic image annotation (AIA) is an image retrieval mechanism to extract relative semantic tags from visual content. So far, the improvement of accuracy in newly developed such methods have been about 1 or 2% in the F1-score and the architectures seem to have room for improvement. Therefore, the authors designed a more detailed architecture for AIA and suggested new algorithms for its main parts. The proposed architecture has three main parts: feature extraction, learning, and annotation. They designed a novel learning method using machine learning and probability bases. In the annotation part, they suggest a novel method that gains the maximum benefit from the learning part. The combination of the proposed architecture, algorithms, and novel ideas resulted in new accuracy milestones in F1-score on most commonly used datasets. In their architecture, N+ measure which shows the number of tags with non-zero recalls showed that they could recall all tags for IAPRTC-12 and ESP-Games datasets. © The Institution of Engineering and Technology 2020
  6. Keywords:
  7. Image analysis ; Image enhancement ; Image retrieval ; Learning systems ; Semantics ; Automatic image annotations (AIA) ; F1 scores ; Learning methods ; Proposed architectures ; Retrieval mechanisms ; Semantic tags ; Visual content ; Image annotation
  8. Source: IET Computer Vision ; Volume 14, Issue 5 , August , 2020 , Pages 214-223
  9. URL: https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2019.0500