DOI QR코드

DOI QR Code

이용자 반응 기반 이미지 감정 접근점 확장에 관한 연구

An Expansion of Affective Image Access Points Based on Users' Response on Image

  • 정은경 (이화여자대학교 사회과학대학 문헌정보전공)
  • 투고 : 2014.08.23
  • 심사 : 2014.09.12
  • 발행 : 2014.09.30

초록

컴퓨터 정보기술의 발전과 함께 감정 기반 컴퓨팅이 다양한 분야에 빠르게 발전하여 확산되고 있다. 감정 기반 컴퓨팅의 지속적인 발전을 위해서는 이미지와 같은 멀티미디어의 콘텐츠의 감정 기반 색인과 검색이 필수적이다. 그러나 감정과 같은 추상적 개념은 주관적이며 이미지의 하위 수준 속성에서 유추하는데 한계가 있기 때문에 감정 색인은 통상적으로 난제로 여겨진다. 본 연구는 감정 색인 개선을 위해서 이미지에 대하여 이용자가 느끼는 감정 반응을 활용하여 이미지를 감정으로 접근하는데 있어서 확장된 접근점을 제공하는 방안을 고찰하였다. 이를 위하여 유로피아나 DB에서 사랑, 행복, 분노, 공포, 슬픔의 5가지 기본 감정을 표현한 이미지 15건을 선정하여 20명의 연구 참여자에게 보여주고 용어를 수집하였다. 이용자의 이미지 반응에서 수집한 용어는 정련 후 총 399건의 고유한 용어로 나타났다. 고유한 399건의 용어는 전체 1,093회 출현하였으며, 동시출현단어분석을 수행하여 상위 출현한 용어 네트워크를 구현하였다. 동시출현단어분석 기반의 네트워크를 통해서 기본 감정 용어와 함께 빈번하게 출현하는 용어를 규명하였다. 이를 통해 기본 감정용어와 함께 확장되어 제시될 수 있는 용어는 형용사, 동작/행위 표현 등 다양하게 나타났다.

Given the context of rapid developing ubiquitous computing environment, it is imperative for users to search and use images based on affective meanings. However, it has been difficult to index affective meanings of image since emotions of image are substantially subjective and highly abstract. In addition, utilizing low level features of image for indexing affective meanings of image has been limited for high level concepts of image. To facilitate the access points of affective meanings of image, this study aims to utilize user-provided responses of images. For a data set, emotional words are collected and cleaned from twenty participants with a set of fifteen images, three images for each of basic emotions, love, sad, fear, anger, and happy. A total of 399 unique emotion words are revealed and 1,093 times appeared in this data set. Through co-word analysis and network analysis of emotional words from users' responses, this study demonstrates expanded word sets for five basic emotions. The expanded word sets are characterized with adjective expression and action/behavior expression.

키워드

참고문헌

  1. 유소영, 문성빈. 2004. 심미적 인상을 이용한 이미지 검색에 관한 실험적 연구. 정보관리학회지, 21(4): 187-208. (Yu, So-Young and Sung-Been Moon. 2004. "An Exploratory Study of Image Retrieval Using Aesthetic Impressions." Journal of the Korean Society for Information Management, 21(4): 187-208.) https://doi.org/10.3743/KOSIM.2004.21.4.187
  2. 이지연. 2002. 이용자 관점에서 본 이미지 색인의 객관성에 대한 연구. 정보관리학회지, 19(3): 123-143. (Lee, Jee-Yeon. 2002. "An Investigation of the Objectiveness of Image Indexing from Users' Perspectives." Journal of the Korean Society for Information Management, 19(3): 123-143.) https://doi.org/10.3743/KOSIM.2002.19.3.123
  3. 정선영, 정은경. 2014. 이미지 감정색인을 위한 시각적 요인 분석에 관한 탐색적 연구. 한국문헌정보학회지, 48(1): 53-73. (Chung, SunYoung and EunKyung Chung. 2014. "An Exploratory Investigation on Visual Cues for Emotional Indexing of Image." Journal of the Korean Society for Library and Information Science, 48(1): 53-73.) https://doi.org/10.4275/KSLIS.2014.48.1.053
  4. Armitage, Linda H. and Peter G. B. Enser. 1997. "Analysis of User Need in Image Archives." Journal of Information Science, 23: 287-299. https://doi.org/10.1177/016555159702300403
  5. Chung, Eun Kyung and Jung Won 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: An International Electronic Journal, 14(3).
  6. Enser, Peter. G. B., Christine. J. Sandom, Jonathon. S. Hare, and Paul H. Lewis. 2007. "Facing the Reality of Semantic Image Retrieval." Journal of Documentation, 63(4): 465-481. https://doi.org/10.1108/00220410710758977
  7. Europeana. .
  8. Fidel, Raya. 1994. "User-centered Indexing." Journal of the American Society for Information Science, 45: 572-576. https://doi.org/10.1002/(SICI)1097-4571(199409)45:8<572::AID-ASI11>3.0.CO;2-X
  9. Fidel, Raya. 1997. "The Image Retrieval Task: Implications for the Design and Evaluation of Image Databases." The New Review Hypermedia and Multimedia, 3: 181-200. https://doi.org/10.1080/13614569708914689
  10. He, Qin. 1999. "Knowledge Discovery through Co-Word Analysis." Library Trends, 48(1): 133-159.
  11. J rgensen, Corinne. 1998. "Attributes of Images in Describing Tasks." Information Processing & Management, 34(2-3): 161-174.
  12. J rgensen, Corinne. 2003. Image Retrieval: Theory and Research. Lanham, MD: Scarecrow Press.
  13. J rgensen, Corinne. 2004. "The Visual Indexing Vocabulary: Developing a Thesaurus for Indexing Images across Diverse Domains." Proceedings of the American Society for Information Science and Technology, 41(1): 287-293.
  14. Knautz, Kathrin and Wolfgang G. Stock. 2011. "Collective Indexing of Emotions in Videos." Journal of Documentation, 67(6): 975-994. https://doi.org/10.1108/00220411111183555
  15. O'Connor, Brian, Mary O'Connor, and June 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
  16. Rho, Seungmin and Sang-Soo Yeo. 2013. "Bridging the Semantic Gap in Multimedia Emotion/Mood Recognition for Ubiquitous Computing Environment." The Journal of Supercomputing, 65(1): 274-286. https://doi.org/10.1007/s11227-010-0447-6
  17. Robertson, Stephen E., M. E. Maron, and William S. Cooper. 1982. "Probability of Relevance: A Unification of Two Models for document retrieval." Information Technology: Research and Development, 1: 1-21.
  18. Rorissa, Abebe. 2008. "User-generated Descriptions of Individual Images versus Labels of Groups of Images: A Comparison Using Basic Level Theory." Information Processing & Management, 44(5): 1741-1753. https://doi.org/10.1016/j.ipm.2008.03.004
  19. Rorissa, Abebe. 2010. "A Comparative Study of Flickr Tags and Index Terms in a General Image Collection." Journal of the American Society for Information Science and Technology, 61(11): 2230-2242. https://doi.org/10.1002/asi.21401
  20. Rosch, Eleanor, Carolyn B. Mervis, Wayne D. Gray, David M. Johnson, and Penny Boyes-Braem. 1976. "Basic Objects in Natural Categories." Cognitive Psychology, 8: 382-439. https://doi.org/10.1016/0010-0285(76)90013-X
  21. Schmidt, Stefanie and Wolfgang G. Stock. 2009. "Collective Indexing of Emotions in Images: a Study in Emotional Information Retrieval." Journal of the American Society for Information Science and Technology, 60(5): 863-876. https://doi.org/10.1002/asi.21043
  22. Shatford Layne, Sara. 1994. "Some Issues in the Indexing of Images." Journal of the American Society for Information Science, 45: 583-588. https://doi.org/10.1002/(SICI)1097-4571(199409)45:8<583::AID-ASI13>3.0.CO;2-N
  23. Stvilia, Besiki and Corinne J rgensen. 2009. "User-generated Collection-level Metadata in an Online Photo-sharing System." Library & Information Science Research, 31(1): 54-65. https://doi.org/10.1016/j.lisr.2008.06.006
  24. Stvilia, Besiki, Corinne J rgensen, and Shuheng Wu. 2012. "Establishing the Value of Sociallycreated Metadata to Image Indexing." Library & Information Science Research, 34: 99-109. https://doi.org/10.1016/j.lisr.2011.07.011
  25. Tao, Jianhua and Tieniu Tan. 2005. "Affective Computing: A Review." Affective Computing and Intelligent Interaction. Lecture Notes in Computer Science, 3784: 981-995.
  26. Wang, Shangfei and Xufa Wang. 2005. "Emotion Semantics Image Retrieval: an Brief Overview." Lecture Notes in Computer Science, 3784: 490-497.
  27. Yoon, Jung Won. 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

피인용 문헌

  1. 이용자 중심의 이미지 접근과 이용 분석을 통한 차세대 멀티미디어 검색 패러다임 요소에 관한 연구 vol.51, pp.4, 2014, https://doi.org/10.4275/kslis.2017.51.4.121