Volume 5, Issue 3 (12-2018)
Human Information Interaction 2018, 5(3): 15-27 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
khademizadeh S, osareh F, Mobini K. A Comparing between the impacts of text based indexing and folksonomy on ranking of images search via Google search engine. Human Information Interaction 2018; 5 (3)
URL: http://hii.khu.ac.ir/article-1-2785-en.html
URL: http://hii.khu.ac.ir/article-1-2785-en.html
Shahid Chamran University of Ahvaz
Abstract: (6399 Views)
Background and Aim: The purpose of this study was to compare the impact of text based indexing and folksonomy in image retrieval via Google search engine.
Methods: This study used experimental method. The sample is 30 images extracted from the book “Gray anatomy”. The research was carried out in 4 stages; in the first stage, images were uploaded to an “Instagram” account so the images are tagged with 600 contacts. In the second stage, the images were uploaded onto 2 blogs using text-based and folksonomy indexing, respectively. In the third stage, 118 medical experts were asked to find one of the images in Google’s image search engine. Finally, in the fourth stage, the rank of the retrieved images from the 2 blogs was reviewed.
Results: Based on the findings; in descriptive analysis, the scores of retrieved images was calculated and in the inferential analysis, independent Chi2 test was used to compare the search results of two blogs. The reported difference was significant.
Conclusion: The results showed that the folksonomy improves images’ retrieval by Google search engine compared to the text-based indexing.
Methods: This study used experimental method. The sample is 30 images extracted from the book “Gray anatomy”. The research was carried out in 4 stages; in the first stage, images were uploaded to an “Instagram” account so the images are tagged with 600 contacts. In the second stage, the images were uploaded onto 2 blogs using text-based and folksonomy indexing, respectively. In the third stage, 118 medical experts were asked to find one of the images in Google’s image search engine. Finally, in the fourth stage, the rank of the retrieved images from the 2 blogs was reviewed.
Results: Based on the findings; in descriptive analysis, the scores of retrieved images was calculated and in the inferential analysis, independent Chi2 test was used to compare the search results of two blogs. The reported difference was significant.
Conclusion: The results showed that the folksonomy improves images’ retrieval by Google search engine compared to the text-based indexing.
Keywords: Image indexing, Folksonomy, image storing and retrieval, social platform, text-based Image Indexing, Google search engine, images ranking, social tagging
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
