Multifaceted image similarity criteria as revealed by sorting tasks

Abstract

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.

Visual Quality Method in Assessing Landscape Characteristics: Case Study of Bozcaada Island

“…Various image sorting procedures and subsequent category naming have been utilized in studies of image similarity see e.g. 41 and may be applied to image quality evaluations as well. Engeldrum 37 discusses different approaches to defining the quality attributes used in scaling: defining the attribute in the textual/verbal instructions, use of visual references as anchors and letting the viewer define the attribute using some internal criteria.…”

Comparison of categorization criteria across image genres

“…Most previous studies have involved a descriptive analysis of the category labels used but have not generated a hierarchical image categorization framework. Laine‐Hernandez and Westman (2008), however, employed a qualitative data‐based analysis on the category names given by their participants and produced the framework for magazine image categorization shown in Table 1.…”

“…between people, animals and inanimate objects as well as according to whether the scenes and objects in the images are man‐made or natural, e.g. buildings vs. landscapes (Laine‐Hernandez & Westman, 2008; Mojsilovic & Rogowitz, 2001; Teeselink et al, 2000). Laine‐Hernandez and Westman (2006) found that people also create image categories based on abstract concepts related to emotions or atmosphere, cultural references and visual elements.…”

“…">discover which image attributes (similarity criteria) are used when categorizing stock photographs and abstract images, and validate the class framework of Laine‐Hernandez & Westman (2008) by studying whether it can be generalized across image genres.…”

Toward a conceptual framework of key‐frame extraction and storyboard display for video summarization

“…The content-based techniques for key-frame extraction have not yet been developed to the point where they are robust or effective in dealing with large collections (Smeaton, Wilkins, Worring, de Rooij, Chua, & Luan, 2008). displacement/atmosphere, emotional cue) Rorissa & Iyer (2008) basic (e.g., chair) subordinate superordinate (e.g., furniture) -(e.g., rocking chair) Laine-Hermandez & visual (color, composition, people (person, social affective (emotion, mood), photography Westman (2008) shape), meta-description status, etc. ), object (buildings, theme, function (illustration, (distance, B & W, (property, number) animals, etc.…”