TY  - JOUR
T1  - A Survey Automatic Image Annotation Based on Machine Learning Models
AU - Mundher Adnan, Myasar AU - Shafry Mohd Rahim, Mohd AU - Muneer Khaleel, Siraj AU - Al-Jawaheri, Karrar 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 20
SP  - 7627
EP  - 7635
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.7627.7635
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.7627.7635
KW  - Image annotation
KW  -AIA
KW  -machine learning
KW  -image retrieval
KW  -development of new techniques
KW  -emphasis
AB  - Image annotation has recently received much attention as a result of the rapid growth in image data.
Several works have been proposed on AIA, especially, in the probabilistic modeling and classification-based
methods. This study presents a review of the image annotation methods which has been published in the last
20 years. Emphasis is mainly on the machine learning models and the classification of the AIA methods into
5 categories of decision tree-based, Support Vector Machine (SVM)-based, k-Nearest Neighbor (kNN)-based,
Deep Neural Network (DNN)-based and Bayesian-based AIAs. A comparison of the five types of AIA
approaches was presented based on the underlying idea, feature extraction method, annotation accuracy,
computational complexity and datasets. Furthermore, a review and explanation of the evaluation metrics used
were presented. Emphasis was also placed on the to carefully consider these aspects during the development
of new techniques and datasets for future image annotation tasks.
ER  -