Automatic image annotation based on deep learning models: A systematic review and future challenges

Adnan, Myasar Mundher and Mohd. Rahim, Mohd. Shafry and Rehman, Amjad and Mehmood, Zahid and Saba, Tanzila and Naqvi, Rizwan Ali (2021) Automatic image annotation based on deep learning models: A systematic review and future challenges. IEEE Access, 9 . pp. 50253-50264. ISSN 2169-3536

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Official URL: http://dx.doi.org/10.1109/ACCESS.2021.3068897

Abstract

Recently, much attention has been given to image annotation due to the massive increase in image data volume. One of the image retrieval methods which guarantees the retrieval of images in the same way as texts are automatic image annotation (AIA). Consequently, numerous studies have been conducted on AIA, particularly on the classification-based and probabilistic modeling techniques. Several image annotation techniques that performed reasonably on standard datasets have been developed over the last decade. In this paper, a review of the image annotation method was conducted, focusing more on deep learning models. Automatic image annotation (AIA) methods were also classified into five categories, including i) Convolutional Neural Network (CNN) based on AIA, ii) Recurrent Neural Network (RNN) based on AIA, iii) Deep Neural Networks (DNN) based on AIA, iv) Long-Short-Term Memory (LSTM) based on AIA, and v) Stacked auto-encoder (SAE) based on AIA. An assessment of the five varieties of AIA methods was also offered based on their principal notion, feature mining technique, explanation precision, computational density, and examined aggregated data. Moreover, the evaluation metrics used to evaluate AIA methods were reviewed and discussed. The need for careful consideration of methods throughout the improvement of novel procedures and datasets for image annotation assignment was highly demanded. From the analysis of the achievements so far, it is certain that more attention should be paid to automatic image annotation.

Item Type:Article
Uncontrolled Keywords:deep learning, digital learning
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computing
ID Code:95896
Deposited By: Widya Wahid
Deposited On:22 Jun 2022 15:19
Last Modified:22 Jun 2022 15:19

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