An analysis of failed queries for web image retrieval

This paper examines a large number of failed queries submitted to a web image search engine, including real users' search terms and written requests. The results show that failed image queries have a much higher specificity than successful queries because users often employ various refined types to specify their queries. The study explores the refined types further, and finds that failed queries consist of far more conceptual than perceptual refined types. The widely used content-based image retrieval technique, CBIR, can only deal with a small proportion of failed queries; hence, appropriate integration of concept-based techniques is desirable. Based on using the concepts of uniqueness and refinement for categorization, the study also provides a useful discussion on the gaps between image queries and retrieval techniques. The initial results enhance the understanding of failed queries and suggest possible ways to improve image retrieval systems.

[1]  Mary F. Casserly,et al.  Usage and Usability Assessment: Library Practices and Concerns , 2002 .

[2]  Edie M. Rasmussen,et al.  Searching for images: The analysis of users' queries for image retrieval in American history , 2003, J. Assoc. Inf. Sci. Technol..

[3]  Amanda Spink,et al.  Real life, real users, and real needs: a study and analysis of user queries on the web , 2000, Inf. Process. Manag..

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

[5]  Hsin-Liang Chen,et al.  An analysis of image queries in the field of art history , 2001, J. Assoc. Inf. Sci. Technol..

[6]  Kevin Roddy.  Subject access to visual resources: What the 90s might portend , 1991 .

[7]  Rachael Green Clemens,et al.  Usage and Usability Assessment: Library Practices and Concerns , 2003 .

[8]  Allan H. Gilbert,et al.  Studies In Iconology: Humanistic Themes In The Art Of The Renaissance , 1939 .

[9]  Pirkko Oittinen,et al.  Image retrieval by end-users and intermediaries in a journalistic work context , 2006, IIiX.

[10]  Shui-Lung Chuang,et al.  Subject categorization of query terms for exploring Web users' search interests , 2002, J. Assoc. Inf. Sci. Technol..

[11]  Peter G. B. Enser,et al.  Towards a Comprehensive Survey of the Semantic Gap in Visual Image Retrieval , 2003, CIVR.

[12]  K. Markey.  Access to iconographic research collections , 1988 .

[13]  Dan I. Moldovan,et al.  Exploiting ontologies for automatic image annotation , 2005, SIGIR '05.

[14]  Raya Fidel,et al.  The image retrieval task: implications for the design and evaluation of image databases , 1997, New Rev. Hypermedia Multim..

[15]  Susanne Ornager.  Image Retrieval: Theoretical Analysis and Empirical User Studies on Accessing Information in Images. , 1997 .

[16]  Monika Henzinger,et al.  Analysis of a very large web search engine query log , 1999, SIGF.

[17]  Corinne Jörgensen,et al.  Image querying by image professionals , 2005, J. Assoc. Inf. Sci. Technol..

[18]  James M. Turner.  Subject Access to Pictures: Considerations in the Surrogation and Indexing of Visual Documents for Storage and Retrieval , 1993 .

[19]  Shih-Fu Chang,et al.  Conceptual framework for indexing visual information at multiple levels , 1999, Electronic Imaging.

[20]  Corinne Jörgensen,et al.  Image querying by image professionals: Research Articles , 2005 .

[21]  Sheena Milne.  When image is everything: Diagnostic imaging , 2008 .

[22]  Samantha Kelly Hastings,et al.  Query Categories in a Study of Intellectual Access to Digitized Art Images. , 1995 .

[23]  Peter G. B. Enser,et al.  Visual image retrieval: seeking the alliance of concept-based and content-based paradigms , 2000, J. Inf. Sci..

[24]  Sara Shatford,et al.  Analyzing the Subject of a Picture: A Theoretical Approach , 1986 .

[25]  Bernard J. Jansen,et al.  A review of web searching studies and a framework for future research , 2001 .

[26]  Jonathon S. Hare,et al.  Mind the gap: another look at the problem of the semantic gap in image retrieval , 2006, Electronic Imaging.

[27]  Hsiao-Tieh Pu.  An analysis of Web image queries for search , 2003, ASIST.

[28]  Marcel Worring,et al.  Classification of user image descriptions , 2004, Int. J. Hum. Comput. Stud..

[29]  Peter G. B. Enser,et al.  Analysis of user need in image archives , 1997, J. Inf. Sci..

[30]  A. T. Schreiber,et al.  Semantic Annotation of Image Collections , 2003 .

[31]  Corinne Jörgensen,et al.  Attributes of Images in Describing Tasks , 1998, Inf. Process. Manag..

[32]  J. Eakins.  Techniques for image retrieval , 1998 .

[33]  Djemel Ziou,et al.  Image Retrieval from the World Wide Web: Issues, Techniques, and Systems , 2004, CSUR.

[34]  Amanda Spink,et al.  Image searching on the Excite Web search engine , 2001, Inf. Process. Manag..

[35]  Karen Markey.  Access to Iconographical Research Collections. , 1988 .

[36]  Shih-Fu Chang,et al.  Image and video search engine for the World Wide Web , 1997, Electronic Imaging.

[37]  Eero Sormunen,et al.  End-User Searching Challenges Indexing Practices in the Digital Newspaper Photo Archive , 2004, Information Retrieval.

[38]  A. Strauss,et al.  The Discovery of Grounded Theory , 1967 .

[39]  Peter G. B. Enser,et al.  Progress in Documentation Pictorial Information Retrieval , 1995, J. Documentation.

[40]  R. Manmatha,et al.  Automatic image annotation and retrieval using cross-media relevance models , 2003, SIGIR.

[41]  Heather P. Jespersen,et al.  The Problem of Subject Access to Visual Materials , 2004 .

[42]  Hugh Griffin,et al.  The democratic indexing of images , 1996, New Rev. Hypermedia Multim..