Journal of the Korean Society for information Management (정보관리학회지)

Korean Society for Information Management (한국정보관리학회)
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Examining Categorical Transition and Query Reformulation Patterns in Image Search Process

이미지 검색 과정에 나타난 질의 전환 및 재구성 패턴에 관한 연구

  • Chung, Eun-Kyung (Department of Library and Information Science, Ewha Womans University) ;
  • Yoon, Jung-Won (School of Library and Information Science, University of South Florida)
  • Received : 2010.05.13
  • Accepted : 2010.06.13
  • Published : 2010.06.30
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Abstract

The purpose of this study is to investigate image search query reformulation patterns in relation to image attribute categories. A total of 592 sessions and 2,445 queries from the Excite Web search engine log data were analyzed by utilizing Batley's visual information types and two facets and seven sub-facets of query reformulation patterns. The results of this study are organized with two folds: query reformulation and categorical transition. As the most dominant categories of queries are specific and general/nameable, this tendency stays over various search stages. From the perspective of reformulation patterns, while the Parallel movement is the most dominant, there are slight differences depending on initial or preceding query categories. In examining categorical transitions, it was found that 60-80% of search queries were reformulated within the same categories of image attributes. These findings may be applied to practice and implementation of image retrieval systems in terms of assisting users' query term selection and effective thesauri development.

이 연구는 이미지 특성 범주와 관련하여 질의 재구성 패턴을 탐색하고자 하였다. 이러한 연구 목적을 수행하기 위해서 Excite 웹검색 엔진 로그 데이터가 사용되었으며, 총 592 세션과 2,445 질의어가 분석되었다. 데이터 분석은 Batley의 정보 형태 구분과 선행 연구에서 밝혀진 팻싯과 서브팻싯을 활용하여 수행되었다. 분석결과는 두가지 형태로 구분하여 제시되었다. 첫째, 질의 재구성에 관한 분석결과이다. 질의 분석 결과, 가장 많은 부분을 차지하는 범주는 특정어(specific)와 지칭어(nameable)이며, 이러한 경향은 다양한 정보 탐색 단계에서도 지속적으로 나타났다. 둘째, 질의 재구성 패턴과 관려하여, 평행이동이 가장 많이 나타났으며, 이러한 경향은 최초 혹은 직전 질의 범주에 따라 근소한 차이를 보였다. 범주 전환 분석에서는 높은 비율(60%-80%)로 검색 질의의 범주가 지속적으로 동일한 범주에 머무르는 경향을 밝혀내었다. 이러한 결과는 이미지 검색 시스템 설계와 구현에 있어서, 이용자의 질의 선정 과정에 도움을 제공하고 효과적인 시소러스 구축 등에 활용될 수 있을 것으로 기대된다.

Keywords

References

  1. Batley, S. 1988. “Visual information retrieval: Browsing strategies in pictorial database.” In Proceedings of 12th International Online Information Meeting, 373-381.
  2. Bruza, P. D. and S. Dennis. 1997. “Query reformulation on the Internet: empirical data and the hyperindex search engine.” In Proceedings of the 5th RIAO Conference.
  3. Chen, H. 2001. “An analysis of image retrieval tasks in the field of art history.” Information Processing & Management, 37(5): 701-720. https://doi.org/10.1016/S0306-4573(00)00049-2
  4. Choi, Y. and E. M. Rasmussen. 2003. “Searching for images: The analysis of users’ queries for image retrieval in American history.” Journal of the American Society for Information Science and Technology, 54(6): 498-511. https://doi.org/10.1002/asi.10237
  5. Chung, E. and J. Yoon. 2009. “Categorical and specificity differences between user-supplied tags and search query terms for images. An analysis of Flickr tags and Web image search queries.” Information Research, 14(3), paper 408. [cited 2009.8.15] .
  6. Collins, K. 1998. “Providing subject access to images: A study of user queries.” The American Archivist, 61: 36-55. https://doi.org/10.17723/aarc.61.1.b531vt5q0q620642
  7. Eakins, J. and M. Graham. 1999. Content-Based Image Retrieval: a Report to the JISC Technology Applications Program. Institute for Image Data Research, University of Northuinbria at Newcastle. [cited 2009.8.15] .
  8. Eastman, C. and B. J. Jansen. 2003. “Coverage, relevance, and ranking: the impact of query operators on Web search engine results.” ACM Transactions on Information Systems, 21(4): 383-411. https://doi.org/10.1145/944012.944015
  9. Efthimiadis, E. N. 1996. “Query expansion.” Annual Review of Information Systems and Technology, 31: 121-187.
  10. Enser, P. G. B., and C. G. McGregor. 1992. Analysis of Visual Information Retrieval Queries: British Library R&D Report 6104. London: British Library.
  11. Enser, P. G. B., C. J. Sandom, J. S. Hare, and P. H. Lewis. 2007. “Facing the reality of semantic image retrieval.” Journal of Documentation, 63(4): 465-481. https://doi.org/10.1108/00220410710758977
  12. Goodrum, A., M. M. Bejune, and A. C. Siochi. 2003. “A state transition analysis of image search patterns on the Web.” Lecture Notes in Computer Science, 2728: 281-290. https://doi.org/10.1007/3-540-45113-7_28
  13. Goodrum, A and A. Spink. 2001. “Image searching on the Excite Web search engine.” Information Processing & Management, 37: 295-311. https://doi.org/10.1016/S0306-4573(00)00033-9
  14. Griesdorf, H and B. O’Connor. 2002. “Modeling what users see when they look at images: a cognitive view point.” Journal of Documentation, 58(1): 1-24
  15. Hastings, S. K. 1995. “Query categories in a study of intellectual access to digitized art images.” Proceedings of the American Society for Information Science, 32: 3-8.
  16. Jansen, B. J. and A. Spink. 2005. “How are we searching the World Wide Web?: an analysis of nine search engine transaction logs.” Information Processing & Management, 42(1): 248-263. https://doi.org/10.1016/j.ipm.2004.10.007
  17. Jorgensen, C. 1998. “Attributes of images in describing tasks.” Information Processing & Management, 34(2/3): 161-174. https://doi.org/10.1016/S0306-4573(97)00077-0
  18. Jorgensen, C. 2003. Image Retrieval: Theory and Research. Scarecrow, Lanham, Md.
  19. Jorgensen, C. and O. Jorgensen. 2005. “Image querying by image professionals.” Journal of the American Society for Information Science and Technology, 56(12): 1346-1359. https://doi.org/10.1002/asi.20229
  20. Keister, L. A. 1994. “User types and queries: Impact on image access systems.” In: R. Fidel, T.B. Hahn, E. M. Rasmussen, and P. J. Smith (eds.), Challenges in Indexing Electronic Text and Images.
  21. Laine-Hernandez, M and S. Westman. 2006. “Image semantics in the description and categorization of journalistic photographs.” Proceedings of the American Society for Information Science and Technology, 43: 1-25.
  22. Lau, T. and E. Horvitz. 1999. “Patterns of search: Analyzing and modeling Web query refinement.” In Proceedings of the 7th International Conference on User Modelling, 119-128.
  23. Manning, C. D., P. Raghaven, and H. Schutze. 2008. Introduction to Information Retrieval. Cambridge: Cambridge University Press.
  24. Marchionini, G. 2005. Information Seeking in Electronic Environments. Cambridge university press, Cambridge.
  25. O’Connor, B., M. O’Connor, and J. Abbas. 1999. “User reactions as access mechanism: An exploration based on captions for images.” Journal of the American Society for Information Science, 50(8): 681-697. https://doi.org/10.1002/(SICI)1097-4571(1999)50:8<681::AID-ASI6>3.0.CO;2-J
  26. Ornager, S. 1997. “Image retrieval: Theoretical analysis and empirical user studies on accessing information in images.” Proceedings of the 60th Annual Meeting of the American Society for Information Science, 34: 202-211.
  27. Rieh, S. Y. and H. Xie. 2006. “Analysis of multiple query reformulations on the web: The interactive information retrieval context.” Information Processing & Management, 42: 751-768. https://doi.org/10.1016/j.ipm.2005.05.005
  28. Rorissa, A. and S. K. Hastings. 2004. “Free sorting of images: Attributes used for categorization.” Proceedings of the American Society for Information Science and Technology, 41: 360-366. https://doi.org/10.1002/meet.1450410142
  29. Shatford, L. 1986. “Analyzing the subject of a picture: A theoretical approach.” Cataloguing & Classification Quarterly, 6(3): 39-62.
  30. Spink, A., B. J. Jansen, D. Wolfram, and T. Saracevic. 2002. “From E-sex to E-commerce: Web search changes.” IEEE Computer, 35(3): 133-135. https://doi.org/10.1109/2.976928
  31. Sutcliffe, A. G., M. Ennis, and S. J. Watkinson. 2000. “Empirical studies of end-user information searching.” Journal of the American Society for Information Science and Technology, 51(13): 1211-1231. https://doi.org/10.1002/1097-4571(2000)9999:9999<::AID-ASI1033>3.0.CO;2-5
  32. Vakkari, P., M. Pennanen, and S. Serola. 2003. “Changes in search terms and tactics while writing a research proposal: A longitudinal case study.” Information Processing & Management, 39: 445-463. https://doi.org/10.1016/S0306-4573(02)00031-6
  33. Yoon, J. 2009. “Towards a user-oriented thesaurus for non-domain-specific image collections.” Information Processing & Management, 45(4): 452-468. https://doi.org/10.1016/j.ipm.2009.03.004

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