A graph based hybrid semi-supervised approach for automatic image annotation

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Kordabadi Mojtaba; MANSOORIZADEH MUHARRAM; KHOTANLOU HASSAN

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: MACHINE VISION AND IMAGE PROCESSING Year:2020 | Volume:6 | Issue:2 Page(s): 79-88

Download Full-Text

Persian Verion

View:

659

Download:

Cites:

Information Journal Paper

Title

A graph based hybrid semi-supervised approach for automatic image annotation

Author(s)

Kordabadi Mojtaba | MANSOORIZADEH MUHARRAM | KHOTANLOU HASSAN | Issue Writer Certificate

Keywords

image retrieval

automatic image annotation

semi-supervised learning

Abstract

Graph based semi-supervised methods for automatic image annotation are mainly focused on single-label problems. However, most of the real world problems require multiple labels per image. As a hybrid semi-supervised approach, LGC+ML-KNN is proposed for multi-label image annotation. LGC is a graph based semi-supervised learning algorithm that annotates unlabeled samples. Subsequently, ML-KNN learns from many more labeled samples, as compared to the initial training set. Experiments on several datasets confirm that the proposed approach has better accuracy than available methods, especially when a very small portion of the training set are the labeled samples.

Cites

No record.

References

No record.

Cite

APA: Copy

Kordabadi, Mojtaba, MANSOORIZADEH, MUHARRAM, & KHOTANLOU, HASSAN. (2020). A graph based hybrid semi-supervised approach for automatic image annotation. MACHINE VISION AND IMAGE PROCESSING, 6(2 ), 79-88. SID. https://sid.ir/paper/360874/en

Vancouver: Copy

Kordabadi Mojtaba, MANSOORIZADEH MUHARRAM, KHOTANLOU HASSAN. A graph based hybrid semi-supervised approach for automatic image annotation. MACHINE VISION AND IMAGE PROCESSING[Internet]. 2020;6(2 ):79-88. Available from: https://sid.ir/paper/360874/en

IEEE: Copy

Mojtaba Kordabadi, MUHARRAM MANSOORIZADEH, and HASSAN KHOTANLOU, “A graph based hybrid semi-supervised approach for automatic image annotation,” MACHINE VISION AND IMAGE PROCESSING, vol. 6, no. 2 , pp. 79–88, 2020, [Online]. Available: https://sid.ir/paper/360874/en

Related Journal Papers