DocumentCode :
1865055
Title :
Kernel PCA-based semantic feature estimation approach for similar image retrieval
Author :
Ogawa, Takahiro ; Haseyama, Miki
Author_Institution :
Grad. Sch. of Inf. Sci. & Technol., Hokkaido Univ., Sapporo
fYear :
2008
fDate :
12-15 Oct. 2008
Firstpage :
965
Lastpage :
968
Abstract :
A kernel PCA-based semantic feature estimation approach for similar image retrieval is presented in this paper. Utilizing database images previously annotated by keywords, the proposed method estimates unknown semantic features of a query image. First, our method performs semantic clustering of the database images and derives a new map from a nonlinear eigenspace of visual and semantic features in each cluster. This map accurately provides the semantic features for the images belonging to each cluster by using their visual features. Further, in order to select the optimal cluster including the query image, the proposed method monitors errors of the visual features caused by the semantic feature estimation process. Then, even if any semantics of the query image are unknown, its semantic features are successfully estimated by the optimal cluster. Experimental results verify the effectiveness of the proposed method for semantic image retrieval.
Keywords :
feature extraction; image retrieval; principal component analysis; database images; image retrieval; kernel PCA-based semantic feature estimation approach; Content based retrieval; Image databases; Image retrieval; Information retrieval; Information science; Internet; Kernel; Principal component analysis; Spatial databases; Visual databases; Image retrieval; Kernel PCA; Semantic feature;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on
Conference_Location :
San Diego, CA
ISSN :
1522-4880
Print_ISBN :
978-1-4244-1765-0
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2008.4711917
Filename :
4711917
Link To Document :
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