期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：10
页码：571-579
出版社：Science and Information Society (SAI)
摘要：Automatic image caption generation is a challenging
AI problem since it requires utilization of several techniques
from different computer science domains such as computer vision
and natural language processing. Deep learning techniques have
demonstrated outstanding results in many different applications.
However, data augmentation in deep learning, which replicates
the amount and the variety of training data available for learning
models without the burden of collecting new data, is a promising
field in machine learning. Generating textual description for a
given image is a challenging task for computers. Nowadays,
deep learning performs a significant role in the manipulation
of visual data with the help of Convolutional Neural Networks
(CNN). In this study, CNNs are employed to train prediction
models which will help in automatic image caption generation.
The proposed method utilizes the concept of data augmentation
to overcome the fuzziness of well-known image caption generation
models. Flickr8k dataset is used in the experimental work of this
study and the BLEU score is applied to evaluate the reliability
of the proposed method. The results clearly show the stability
of the outcomes generated through the proposed method when
compared to others.
关键词：Convolutional Neural Networks (CNN); image caption
generation; data augmentation; deep learning
