Open AccessMultifaceted image similarity criteria as revealed by sorting tasks
Author(s) -
LaineHernandez Mari,
Westman Stina
Publication year - 2008
Publication title -
proceedings of the american society for information science and technology
Language(s) - English
Resource type - Journals
eISSN - 1550-8390
pISSN - 0044-7870
DOI - 10.1002/meet.2008.1450450256
Subject(s) - similarity (geometry) , categorization , sorting , theme (computing) , photography , artificial intelligence , image (mathematics) , computer science , pattern recognition (psychology) , function (biology) , information retrieval , natural language processing , visual arts , art , evolutionary biology , biology , programming language , operating system
This paper reports a study on the types of image categories constructed from magazine photographs. A novel sorting procedure was tested with the aim of providing more data on image similarity and possible category overlap. Expert and non‐expert participants were compared in their categorizations. The new similarity sorting procedure resulted in an average of 67%–111% increase in similarity data gathered compared to basic free sorting. Categories were constructed on various levels of similarity: image Function, main visual content (People, Objects and Scene), conceptual content (Theme) and descriptors (Story, Affective, Description, Photography and Visual). Most categories were based on the theme and people portrayed in the photograph, and in the case of the expert subjects, image function. Also abstract and syntactic similarity criteria were employed by the subjects. The categories created by each subject showed on average a 35%–53% overlap. Participants also demonstrated a tendency to use multiple similarity criteria simultaneously and to combine terms from different levels in a single category name. These results indicate a need for a multifaceted approach in image categorization.