@article{MAKHILLJEAS2017121614730,
    title = {Image Categorization using Topic Modeling with the Latent Dirichlet Allocation},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {16},
    pages = {4123-4126},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.4123.4126},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.4123.4126},
    author = {Ghaidaa and},
    keywords = {Topic modeling,image text descriptions,LDA,descriptions,encouraging,benchmark},
    abstract = {In this study, the issue of text modeling was considered with an annotated textual description for
diverse categories of images. A topic modeling with the Latent Dirichlet Allocation technique were proposed
on a set of images text descriptions to realize the precise topic for each set of images with respect to their
attached descriptions from which classifying new images based on the observed topics would be more
facilitated. The research was evaluated on the benchmark CLEF dataset; the results were encouraging with
regards to the enhancing image retrieval using topics extraction.}
    }