Paper Info
Title
A study of block-global feature based supervised image annotation
Abstract
In order to get better semantic annotation performance, block-global features are extracted as low-level visual features for image semantic annotation. Specifically, wellknown global feature extraction method, namely two-dimensional principal component analysis (2DPCA) is applied to extract the image block-global features. Unlike typical image annotation methods which use local features or global features separately, we propose to extract global features from image local regions (block) with the expectation of: a) combining the advantages of local and global features; b) discovering multiple semantic meanings in one image. In the experiment, comparative studies have been done for the performance of block-global feature extraction methods with widely used local feature extraction method such as scale invariant feature transform. The results show that 2DPCA has a significantly better performance than the performance of other methods.
Year
DOI
Venue
2011
10.1109/ICSMC.2011.6083795
SMC
Keywords
Field
DocType
image semantic annotation,scale invariant feature transform,semantic annotation performance,supervised image annotation,global feature,feature extraction,image retrieval,block global feature,content based image retrieval,two dimensional principal component analysis,visual perception,low level visual features,global feature extraction method,principal component analysis,content-based retrieval,vectors,accuracy,semantics,covariance matrix
Scale-invariant feature transform,Automatic image annotation,Pattern recognition,Feature detection (computer vision),Feature (computer vision),Computer science,Image retrieval,Feature extraction,Artificial intelligence,Semantics,Principal component analysis
Conference
Volume
Issue
ISSN
null
null
1062-922X
ISBN
Citations 
PageRank 
978-1-4577-0652-3
0
0.34
References 
Authors
14
4
Name
Order
Citations
PageRank
Jing He19314.67
Ziheng Jiang2677.19
Ping Guo360185.05
Lixiong Liu422012.03