Abstract
Automatic image annotation is a crucial area in computer vision, which plays a significant role in image retrieval, image description, and so on. Along with the internet technique developing, there are numerous images posted on the web, resulting in the fact that it is a challenge to annotate images only by humans. Hence, many computer vision researchers are interested in automatic image annotation and make a great effort in optimizing its performance. Automatic image annotation is a task that assigns several tags in a limited vocabulary to describe an image. There are many algorithms proposed to tackle this problem and all achieve great performance. In this paper, we review seven algorithms for automatic image annotation and evaluate these algorithms leveraging different image features, such as color histograms and Gist descriptor. Our goal is to provide insights into the automatic image annotation. A lot of comprehensive experiments, which are based on Corel5K, IAPR TC-12, and ESP Game datasets, are designed to compare the performance of these algorithms. We also compare the performance of traditional algorithms employing deep learning features. Considering that not all associated labels are annotated by human annotators, we leverage the DIA metrics on IAPR TC-12 and ESP Game datasets.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.