An Evaluation of Major Image Search Engines on Various Query Topics

This paper investigates the information retrieval effectiveness of major image search engines based on various query topics. Initially, major image search engines, namely, Google, Yahoo, Ask and MSN are selected. Then, seven appropriate topics are determined from the categories of the top search terms used on the web and five queries per topic are chosen. Each query is run on the selected image search engines separately and first forty images retrieved in each retrieval output are classified as being "relevant" or "non-relevant" to calculate precision ratios at various cut-off points for each pair of query and search engine. The results indicated that Google has the best overall retrieval effectiveness in topics "Automotive Manufacturers", "Broadcast Media", "Pharmaceutical and Medical Product" and "Movies" and was followed by MSN in topics "Food and Beverage Brands", "IT and Internet" and Ask in topic "Travel Destinations and Accommodations". All image search engines seem to have the lowest effectiveness for the topic "Food and Beverage Brands". The precision ratio of any one of the image search engines was not the same and changed for every topic.

[1]  Yiltan Bitirim,et al.  The Impact of Number of Query Words on Image Search Engines , 2007, Second International Conference on Internet and Web Applications and Services (ICIW'07).

[2]  Yasar Tonta,et al.  Information Retrieval Effectiveness of Turkish Search Engines , 2002, ADVIS.

[3]  Hsiao-Tieh Pu,et al.  A comparative analysis of web image and textual queries , 2005, Online Inf. Rev..

[4]  Amanda Spink,et al.  Searching multimedia federated content web collections , 2006, Online Inf. Rev..

[5]  Sen Yang,et al.  Mixed Query Image Retrieval System , 2007, 2007 International Conference on Information Acquisition.

[6]  Xing Xie,et al.  Effective browsing of web image search results , 2004, MIR '04.

[7]  Hai Jin,et al.  Using Implicit Relevane Feedback to Advance Web Image Search , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[8]  Boris Babenko,et al.  ImprovingWeb-based Image Search via Content Based Clustering , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[9]  Ga Young,et al.  Technical Advisory Service for Images , 2004 .

[10]  Toru Fukumoto An analysis of image retrieval behavior for metadata type image database , 2006, Inf. Process. Manag..