We propose a perceptually based system for pattern retrieval and matching. There is a need for such an 'intelligent' retrieval system in applications such as digital museums and libraries, design, architecture, and digital stock photography. The central idea of the work is that similarity judgment has to be modeled along perceptual dimensions. Hence, we detect basic visual categories that people use in judgment of similarity, and design a computational model that accepts patterns as input, and depending on the query, produces a set of choices that follow human behavior in pattern matching. There are two major research aspects to our work. The first one addresses the issue of how humans perceive and measure similarity within the domain of color patterns. To understand and describe this mechanism we performed a subjective experiment. The experiment yielded five perceptual criteria used in comparison between color patterns (vocabulary), as well as a set of rules governing the use of these criteria in similarity judgment (grammar). The second research aspect is the actual implementation of the perceptual criteria and rules in an image retrieval system. Following the processing typical for human vision, we design a system to: 1) extract perceptual features from the vocabulary and 2) perform the comparison between the patterns according to the grammar rules. The modeling of human perception of color patterns is new - starting with a new color codebook design, compact color representation, and texture description through multiple scale edge distribution along different directions. Moreover, we propose new color and texture distance functions that correlate with human performance. The performance of the system is illustrated with numerous examples from image databases from different application domains.
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design