Performance evaluation of web search engines in image retrieval: An experimental study

Abstract

This experimental study used a checklist to evaluate the performance of seven search engines consisting of four Image General Search Engines (IGSEs) (namely, Google, Yahoo DuckDuckGo and Bing), and three Image Specialized Search Engines (ISSEs) (namely, Flicker, PicSearch, and GettyImages) in image retrieval. The findings indicated that the recall average of Image General Search Engines and Image Specialized Search Engines was found to be 76.32% and 24/51% with the precision average of 82/08% and 32/21%, respectively. As the results showed, Yahoo, Google and DuckDuckGo ranked at the top in image retrieval with no significant difference. However, a remarkable superiority with almost 50% difference was observed between the general and specialized image search engines. It was also found that an intense competition existed between Google, Yahoo and DuckDuckGo in image retrieval. The overall results can provide valuable insights for new search engine designers and users in choosing the appropriate search engines for image retrieval. Moreover, the results obtained through the applied equations could be used in assessing and evaluating other search tools, including search engines.

Keywords

image retrieval search engines image search engines image general search engine image specialized search engines performance

Get full access to this article

View all access options for this article.

References

Adrakatti

Wodeyar

Mulla

(2016) Search by image: A novel approach to content based image retrieval system. International Journal of Library Science 14(3): 41–47.

Ali

Beg

MMS

(2011) An overview of Web search evaluation methods. Computers and Electrical Engineering 37(6): 835–848.

Bradlow

Schmittlein

(2000) The little engines that could: Modeling the performance of World Wide Web search engines. Marketing Science 19(1): 43–62.

Çakir

Bahçeci

Bitirim

(2008) An evaluation of major image search engines on various query topics. The Third International Conference on Internet Monitoring and Protection, Bucharest, Romania, pp. 161–165.

Chu

Rosenthal

(1996) Search engines for the World Wide Web: A comparative study and evaluation methodology. ASIS Annual Conference Proceedings, Baltimore, MD, 127–135. Available at: www.asis.org/annual-96/ElectronicProceedings/chu.html (accessed 10 November 2019).

Clarke

Willett

(1997) Estimating the recall performance of Web search engines. Aslib Proceedings 49(7): 184–189.

Deselaers

Keysers

Ney

(2008) Features for image retrieval: An experimental comparison. Information Retrieval 11: 77–107. http://doi:10.1007/s10791-007-9039-3.

Ding

Marchionini

(1996) A comparative study of Web search service performance. Proceedings of the ASIST Annual Meeting, 33, 136–140. Available at: https://uncch.pure.elsevier.com/en/publications/a-comparative-study-of-web-search-service-performance (accessed 19 October 2019).

Fendley

Kidambi

(2010) Benchmarking web-based image retrieval. Proceedings of the Sixteenth Americas Conference on Information Systems, Lima, Peru, 12–15 August, 2010.

10.

George

Elayidom

Santhanakrishnan

(2016) Measures to Evaluate the Superiority of a Search Engine.International Journal on Recent and Innovation Trends in Computing and Communicatio 4(2): 214–220.

11.

Google Image Search: Over 10 billion images, 1 billion pageviews a day (2010) Available at: https://techcrunch.com (accessed 13 November 2019).

12.

Gordon

Pathak

(1999) Finding information on the World Wide Web: The retrieval effectiveness of search engines. Information Processing and Management 35: 141–180.

13.

Hsieh-yee

(1988) Internet: Organization and searching. Information Science 18(3–4): 94–103.

14.

Hussain

Gul

Ahmad Shah

Shueb

(2019) Retrieval effectiveness of image search engines. The Electronic Library 37(1): 173–184.

15.

Internet live stats (2019) Internet live stats. Available at: https://www.internetlivestats.com (accessed 14 November 2019).

16.

Jul

(1991) Howard Besser explores the development of image databases. OCLC Newsletter 190: 16–17.

17.

Lang

Liu

(2013) The next-generation search engine: Challenges and key technologies. In: Gaol

(Eds) Recent progress in data engineering and Internet technology, vol 156. Berlin, Heidelberg: Springer, pp. 239–248.

18.

Leighton

Srivastava

(1999) First 20 precision among World Wide Web search services (search engines). Journal of the American Society for Information Science 50(10): 870–881.

19.

Shang

(2000) A new method for automatic performance comparison of search engines. World Wide Web 3(4): 241–247.

20.

Kobayashi

Agusa

Zhu

(2009) Image semantic search engine. First International Workshop on Database Technology and Applications. IEEE. http://doi:10.1109/DBTA.2009.148.

21.

Merriam-Webster

Inc

(2019) Merriam-Webster Dictionary. Image. Available at: https://www.merriam-webster.com/dictionary/image (accessed 19 October 2019).

22.

Stevenson

Leung

(2005) Comparative evaluation of web image search engines for multimedia applications. IEEE International Conference on Multimedia and Expo. http://doi:10.1109/icme.2005.1521641.

23.

Tokgoz

Ozcılnak

Cınar

Yalun

Bitirim

(2013) An evaluation of Turkish retrieval performance of popular search engines for Internet and image search by using common lists. The Third International Conference on Digital Information and Communication Technology and its Applications, pp. 148–153.

24.

Tuğçe Elagöz

Mendeli

Manioğlular

Bitirim

(2008) An empirical evaluation on meta-image search engines. The Third International Conference on Digital Telecommunications. http://doi:10.1109/ICDT.2008.10

25.

Uluç

Emirzade

Yıltan

(2007) The impact of number of query words on image search engines. Second E. International Conference on Internet and Web Applications and Services (ICIW’07). http://doi:10.1109/iciw.2007.61

26.

Uyar

Karapinar

(2016) Investigating the precision of Web image search engines for popular and less popular entities. Journal of Information Science 43(3): 378–392.

27.

Vaughan

(2004) New measurements for search engine evaluation. Information Processing and Management 40(4): 677–691.