TY - JOUR
T1 - Matching and retrieval based on the vocabulary and grammar of color patterns
AU - Mojsilović, Aleksandra
AU - Kovačević, Jelena
AU - Hu, Jianying
AU - Safranek, Robert J.
AU - Ganapathy, Kicha
PY - 2000/1
Y1 - 2000/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0033895099&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0033895099&partnerID=8YFLogxK
U2 - 10.1109/83.817597
DO - 10.1109/83.817597
M3 - Article
C2 - 18255371
AN - SCOPUS:0033895099
SN - 1057-7149
VL - 9
SP - 38
EP - 54
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 1
ER -