DocumentCode
3214545
Title
A long term learning method in CBIR systems by defining semantic templates
Author
Rashedi, Esmat ; Nezamabadi-pour, Hossein
Author_Institution
Shahid Bahonar Univ. of Kerman, Kerman, Iran
fYear
2012
fDate
15-17 May 2012
Firstpage
1258
Lastpage
1261
Abstract
This paper provides a long term learning method in CBIR systems by defining a kind of semantic template to represent semantic concepts of images. Information, which is achieved in the short learning technique, is used in construction of semantic templates. A similarity function is introduced to find the similarity between queries and semantic templates. The experimental results confirm the effectiveness of the proposed method.
Keywords
content-based retrieval; image retrieval; learning (artificial intelligence); semantic networks; CBIR systems; content based image retrieval system; long term learning method; queries; semantic templates; short learning technique; similarity function; Equations; Image segmentation; Semantics; Content based image retrieval; Long term learning; Semantic template;
fLanguage
English
Publisher
ieee
Conference_Titel
Electrical Engineering (ICEE), 2012 20th Iranian Conference on
Conference_Location
Tehran
Print_ISBN
978-1-4673-1149-6
Type
conf
DOI
10.1109/IranianCEE.2012.6292549
Filename
6292549
Link To Document