Paper Info
Title
Image Retrieval Using Multi-granularity Features of Color and Texture
Abstract
Making full use of image information with its own to extract features is the crucial problem in content based image retrieval (CBIR). In this paper, quotient space(QS) granularity computing theory is imported into image retrieval field, granularity thinking in image retrieval is clarified, and a novel image retrieval method is proposed. Firstly, aiming at the different behaviors under different granularities, color and texture features are obtained respectively under different granularities, different quotient spaces are achieve; secondly, do the attribute combination to the obtained quotient spaces according to the quotient space granularity combination principle; and then realize image retrieval using the combined attribute function. Comparing with methods adopting single attribute feature the image retrieval method based on quotient space granularity combination utilizes the image information with its own in a more effective way. The experimental results demonstrate the feasibility and validity of the proposed method.
Year
DOI
Venue
2008
10.1109/FSKD.2008.36
FSKD (4)
Keywords
Field
DocType
multi-granularity features,different behavior,image retrieval field,different granularity,image retrieval,image retrieval method,image information,quotient space granularity combination,novel image retrieval method,different quotient space,quotient space,color,artificial neural networks,image texture,pixel,granular computing,manganese,texture,feature extraction,histograms
Computer vision,Automatic image annotation,Pattern recognition,Image texture,Computer science,Quotient space (topology),Image retrieval,Feature extraction,Artificial intelligence,Granularity,Content-based image retrieval,Visual Word
Conference
Citations 
PageRank 
References 
1
0.36
8
Authors
5
Name
Order
Citations
PageRank
Xiangli Xu131.91
Libiao Zhang2347.03
Xiangdong Liu320.84
Zhezhou Yu4225.50
Chunguang Zhou554352.37