Similarity measurement for describe user images in social media

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.

Tavakoli Targhi Alireza

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

Journal Paper

Paper Information

Journal: INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS Year:2017 | Volume:8 | Issue:1 Page(s): 291-299

Download Full-Text

View:

203

Download:

174

Cites:

Information Journal Paper

Title

Similarity measurement for describe user images in social media

Author(s)

Tavakoli Targhi Alireza | Issue Writer Certificate

Keywords

Similarity Measurement Web mining

Health Topics

Computer Vision

Machine Learning Models

Abstract

Online social networks like Instagram are places for communication. Also, these media produce rich metadata which are useful for further analysis in many elds including health and cognitive science. Many researchers are using these metadata like hashtags, images, etc. to detect patterns of user activities. However, there are several serious ambiguities like how much reliable are these information. In this paper, we attempt to answer two main questions. Firstly, are image hashtags directly related to image concepts? Can image concepts being predicted using machine learning models? The results of our analysis based on 105000 images on Instagram show that user hashtags are hardly related to image concepts (only 10%of test cases). Second contribution of this paper is showing the suggested pre-trained model predicate image concepts much better (more than 50% of test cases) than user hashtags. Therefore, it is strongly recommended to social media researchers not to rely only on the user hashtags as a label of images or as a signal of information for their study. Alternatively, they can use machine learning methods line deep convolutional neural network model to describe images to extract more related contents. As a proof of concept, some results on food images are studied. We use few similarity measurements to compare result of human and deep convolutional neural network. These analysis is important because food is an important society health eld.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

APA: Copy

Tavakoli Targhi, Alireza. (2017). Similarity measurement for describe user images in social media. INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS, 8(1), 291-299. SID. https://sid.ir/paper/340779/en

Vancouver: Copy

Tavakoli Targhi Alireza. Similarity measurement for describe user images in social media. INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS[Internet]. 2017;8(1):291-299. Available from: https://sid.ir/paper/340779/en

IEEE: Copy

Alireza Tavakoli Targhi, “Similarity measurement for describe user images in social media,” INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS, vol. 8, no. 1, pp. 291–299, 2017, [Online]. Available: https://sid.ir/paper/340779/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops