@article{MAKHILLJEAS2019142018556,
    title = {A Survey Automatic Image Annotation Based on Machine Learning Models},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {20},
    pages = {7627-7635},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.7627.7635},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.7627.7635},
    author = {Myasar,Mohd,Siraj and},
    keywords = {Image annotation,AIA,machine learning,image retrieval,development of new techniques,emphasis},
    abstract = {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.}
    }