Relationships between perceived features and similarity of images: A test of Tversky’s Contrast Model

Loading...
Thumbnail Image

Authors

Rorissa, Abebe

Issue Date

2007

Type

Article

Language

en

Keywords

Similarity , Contrast Model , Images

Research Projects

Organizational Units

Journal Issue

Alternative Title

Abstract

The rapid growth of the numbers of images and their users as a result of the reduction in cost and increase in efficiency of the creation, storage, manipulation, and transmission of images poses challenges to those who organize and provide access to images. One of these challenges is similarity matching, a key component of current content-based image retrieval systems. Similarity matching often is implemented through similarity measures based on geometric models of similarity whose metric axioms are not satisfied by human similarity judgment data. This study is significant in that it is among the first known to test Tversky's contrast model, which equates the degree of similarity of two stimuli to a linear combination of their common and distinctive features, in the context of image representation and retrieval. Data were collected from 150 participants who performed an image description and a similarity judgment task. Structural equation modeling, correlation, and regression analyses confirmed the relationships between perceived features and similarity of objects hypothesized by Tversky. The results hold implications for future research that will attempt to further test the contrast model and assist designers of image organization and retrieval systems by pointing toward alternative document representations and similarity measures that more closely match human similarity judgments.

Description

Citation

Rorissa, A. (2007). Relationships between perceived features and similarity of images: A test of Tversky’s Contrast Model. Journal of the American Society for Information Science and Technology, 58(10), 1401-1418.

Publisher

John Wiley & Sons, Inc.

License

Journal

Volume

Issue

PubMed ID

DOI

ISSN

EISSN