DocumentCode :
1706496
Title :
Minimizing human-machine interactions in automatic image retrieval
Author :
Jarrah, Kambiz ; Muneesawang, Paisarn ; Lee, Ivan ; Guan, Ling
Author_Institution :
Multimedia Res. Lab., Ryerson Polytech. Univ., Toronto, Ont., Canada
Volume :
3
fYear :
2004
Firstpage :
1589
Abstract :
The self-organizing tree map (SOTM) has been successfully applied to image retrieval with automatic relevance feedback, but may provide inaccurate relevance identification when dealing with a complex image database. We developed an improved SOTM method which treats relevance identification as a one (the relevant class) vs. many (the irrelevant classes) problem. Experimental results demonstrated the superior performance of the proposed approach.
Keywords :
content-based retrieval; image retrieval; relevance feedback; self-organising feature maps; unsupervised learning; visual databases; SOTM; automatic image retrieval; automatic relevance feedback; complex image database; content-based retrieval; human-machine interactions; self-organizing tree map; Content based retrieval; Feature extraction; Feedback; Humans; Image databases; Image retrieval; Information retrieval; Libraries; Man machine systems; Neurofeedback;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical and Computer Engineering, 2004. Canadian Conference on
ISSN :
0840-7789
Print_ISBN :
0-7803-8253-6
Type :
conf
DOI :
10.1109/CCECE.2004.1349712
Filename :
1349712
Link To Document :
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