Architecture to improve the accuracy of automatic image annotation systems. Issue 5 (23rd April 2020)

• Record Type: Journal Article
• Authors: Ghostan Khatchatoorian, Artin; Jamzad, Mansour
• Is Part Of: IET computer vision. Volume 14:Issue 5(2020)
• Page Start: 214
• Page End: 223
• Publication Date: 2020-04-23
• Subjects: probability -- learning (artificial intelligence) -- feature extraction -- image retrieval -- image annotation; automatic image annotation systems -- AIA -- image retrieval mechanism -- feature extraction part -- machine learning -- annotation part -- learning part -- semantic tag extraction -- probability bases -- ESP-Games dataset -- IAPRTC-12 dataset; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2019.0500
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : 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.