Indexing natural images for retrieval based on kansei factors

John A. Black, Kanav Kahol, Priyamvada Tripathi, Prem Kuchi, Sethuraman Panchanathan

Research output: Contribution to journalConference articlepeer-review

14 Scopus citations


Current image indexing methods are based on measures of visual content. However, this approach provides only a partial solution to the image retrieval problem. For example, an artist might want to retrieve an image (for use in an advertising campaign) that evokes a particular "feeling" in the viewer. One technique for measuring evoked feelings, which originated in Japan, indexes images based on the inner impression (i.e. the kansei) experienced by a person while viewing an image or object - impressions such as busy, elegant, romantic, or lavish. The aspects of the image that evoke this inner impression in the viewer are called kansei factors. The challenge in kansei research is to enumerate those factors, with the ultimate goal of indexing images with the "inner impression" that viewers experience. Thus, the focus is on the viewer, rather than on the image, and similarity measures derived from kansei indexing represent similarities in inner experience, rather than visual similarity. This paper presents the results of research that indexes images based on a set of kansei impressions, and then looks for correlations between that indexing and traditional content-based indexing. The goal is to allow the indexing of images based on the inner impressions they evoke, using visual content.

Original languageEnglish (US)
Pages (from-to)363-375
Number of pages13
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2004
EventHuman Vision and Electronic Imaging IX - San Jose, CA, United States
Duration: Jan 19 2004Jan 21 2004


  • Affective basis functions
  • Affective content
  • Content based image retrieval
  • High-level content
  • Image content
  • Kansei
  • Lexical basis functions
  • NaturePix
  • Semantic content
  • Visual content

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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