Image retrieval effectiveness of Bing Images, Google Images and Yahoo Image Search in the scientific field of tourism and COVID-19

被引:0
作者
Hussain, Aabid [1 ]
Gul, Sumeer [2 ,5 ]
Nisa, Nahida Tun [3 ]
Shueb, Sheikh [4 ]
Gulzar, Farzana [2 ]
Bano, Shohar [2 ]
机构
[1] Directorate Informat & Publ Relat Jammu & Kashmir, Gauhati, India
[2] Univ Kashmir, Srinagar, India
[3] Amar Singh Coll, Srinagar, India
[4] Islamic Univ Sci & Technol, Awantipora, India
[5] Univ Kashmir, Srinagar 190006, India
关键词
Bing Images; Google Images; image retrieval; image search engine effectiveness; image search engines; search engines; Yahoo Image Search; WORLD-WIDE-WEB; PERFORMANCE EVALUATION; ENGINES; USERS; PRECISION;
D O I
10.1177/01655515231161560
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The year 2020 brought a big concern for the global community because of COVID-19, which affected every sector of society, and tourism is no exception. Researchers across the globe are publishing their studies related to different dimensions of tourism in the context of COVID-19, and images have formed an essential component of their research. In tourism, images related to COVID-19 can open new dimensions for scholars. The main aim of the research is to measure the retrieval effectiveness of three image search engines (ISEs), that is, Bing Images, Google Images and Yahoo Image Search, concerning images related to COVID-19 and tourism. The study attempts to identify the capability of the ISEs to retrieve the desired and actual images related to COVID-19 and tourism. The PubMed Central (PMC) Database was consulted to retrieve the desired images and develop a testbed. The advanced search feature of PMC Database was explored by typing the search terms 'COVID-19' and 'Tourism' using 'AND' operator to make the search more comprehensive. Both the terms were searched against the 'Figure/Table' caption to retrieve papers carrying images related to COVID-19 and tourism. Queries were executed across the select ISEs, that is, Bing Images, Google Images and Yahoo Image Search. Retrieved images were individually analysed against the original image from the articles to determine the Precision, Relative Recall, F-Measure and Fallout Ratio. The format of the images in JPG/JPEG, besides checking the original image rank in the retrieved lot, was also ascertained. Bing Images scores more in terms of Mean Precision, followed by Google Images and Yahoo Image Search. For the Relative Recall measure, Google Images scores high, followed by Bing Images and Yahoo Image Search, respectively. Regarding F-Measure and Fallout Ratio, Bing Images outperforms Google Images and Yahoo Image Search. In retrieving the sought format of JPG/JPEG, Google Images performs best, followed by Yahoo Image Search and Bing Images. Google Images produces the original image at the first rank on more than one occasion. In contrast, Bing Images retrieves the original image at the first rank in two instances. Yahoo Images performs poorly over this metric as it does not retrieve any original image at the first rank on any other instance. The study cannot be generalised as the scope is only limited to the images indexed by PMC. Furthermore, the retrieval effectiveness of only three ISEs is measured. The study is the first to measure the retrieval effectiveness of ISEs in retrieving images related to the COVID-19 pandemic and tourism. The study can be extended across other image-indexing databases pertinent to tourism studies, and the retrieval effectiveness of other ISEs can also be considered.
引用
收藏
页数:10
相关论文
共 69 条
  • [1] Adrakatti A., 2016, INT J LIBR SCI, V14, P41
  • [2] Alomairi Abeer Esa A. M., 2016, Journal of Theoretical and Applied Information Technology, V84, P215
  • [3] [Anonymous], 1979, Information Retrieval.
  • [4] Semantic text-based image retrieval with multi-modality ontology and DBpedia
    Aspura, Yanti Idaya M. K.
    Noah, Shahrul Azman Mohd
    [J]. ELECTRONIC LIBRARY, 2017, 35 (06) : 1191 - 1214
  • [5] COVID-19 and the recovery of the tourism industry
    Assaf, Albert
    Scuderi, Raffaele
    [J]. TOURISM ECONOMICS, 2020, 26 (05) : 731 - 733
  • [6] Balabantaray R. C., 2013, INT J HUM COMPUT INT, V4, P117
  • [7] Brownlee J., 2020, CALCULATE PRECISION
  • [8] Automatic performance evaluation of Web search engines
    Can, F
    Nuray, R
    Sevdik, AB
    [J]. INFORMATION PROCESSING & MANAGEMENT, 2004, 40 (03) : 495 - 514
  • [9] Performance evaluation of web search engines in image retrieval: An experimental study
    CheshmehSohrabi, Mehrdad
    Sadati, Elham Adnani
    [J]. INFORMATION DEVELOPMENT, 2022, 38 (04) : 522 - 534
  • [10] Chu HT, 1996, P ASIS ANNU MEET, V33, P127