TY - GEN
T1 - Image similarity computation using local similarity patterns generated by genetic algorithm
AU - Stejic, Zoran
AU - Iyoda, Eduardo M.
AU - Takama, Yasufumi
AU - Hirota, Kaoru
PY - 2002
Y1 - 2002
N2 - Local similarity pattern (LSP) is proposed as a new method for computing image similarity. Similarity of a pair of images is expressed in terms of similarities of the corresponding image regions, obtained by uniform partitioning of the image area. Different from the conventional methods, each region-wise similarity is computed using a different combination of image features (color, shape, and texture). In addition, a method for optimizing LSP, based on genetic algorithm, is proposed, and incorporated in the relevance feedback process, allowing the user to automatically specify LSP-based queries. LSP is evaluated on four test databases totalling over 2,000 images. Compared with six conventional methods, and SIMPLIcity, an advanced image retrieval system, LSP brings between 15% and 24% increase in the average retrieval precision. LSP, allowing comparison of different image regions using different similarity criteria, is more suited for modeling human perception of image similarity than the conventional methods.
AB - Local similarity pattern (LSP) is proposed as a new method for computing image similarity. Similarity of a pair of images is expressed in terms of similarities of the corresponding image regions, obtained by uniform partitioning of the image area. Different from the conventional methods, each region-wise similarity is computed using a different combination of image features (color, shape, and texture). In addition, a method for optimizing LSP, based on genetic algorithm, is proposed, and incorporated in the relevance feedback process, allowing the user to automatically specify LSP-based queries. LSP is evaluated on four test databases totalling over 2,000 images. Compared with six conventional methods, and SIMPLIcity, an advanced image retrieval system, LSP brings between 15% and 24% increase in the average retrieval precision. LSP, allowing comparison of different image regions using different similarity criteria, is more suited for modeling human perception of image similarity than the conventional methods.
UR - https://www.scopus.com/pages/publications/84901472339
U2 - 10.1109/CEC.2002.1007023
DO - 10.1109/CEC.2002.1007023
M3 - Conference contribution
AN - SCOPUS:84901472339
SN - 0780372824
SN - 9780780372825
T3 - Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002
SP - 771
EP - 776
BT - Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002
PB - IEEE Computer Society
T2 - 2002 Congress on Evolutionary Computation, CEC 2002
Y2 - 12 May 2002 through 17 May 2002
ER -