DocumentCode :
3613413
Title :
Image similarity computation using local similarity patterns generated by genetic algorithm
Author :
Z. Stejic;E.M. Iyoda;Y. Takama;K. Hirota
Author_Institution :
Dept. of Computational Intelligence & Syst. Sci., Tokyo Inst. of Technol., Yokohama, Japan
Volume :
1
fYear :
2002
fDate :
6/24/1905 12:00:00 AM
Firstpage :
771
Abstract :
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.
Keywords :
"Genetic algorithms","Image retrieval","Feedback","Image databases","Information retrieval","Optimization methods","Humans","Computational intelligence","Shape","Testing"
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2002. CEC ´02. Proceedings of the 2002 Congress on
Print_ISBN :
0-7803-7282-4
Type :
conf
DOI :
10.1109/CEC.2002.1007023
Filename :
1007023
Link To Document :
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