Title :
A study of block-global feature based supervised image annotation
Author :
He, Jing ; Jiang, Ziheng ; Guo, Ping ; Liu, Lixiong
Author_Institution :
Sch. of Comput. Sci. & Technol., Beijing Inst. of Technol., Beijing, China
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.
Keywords :
content-based retrieval; feature extraction; image retrieval; principal component analysis; block global feature; content based image retrieval; global feature extraction method; low level visual features; scale invariant feature transform; semantic annotation performance; supervised image annotation; two dimensional principal component analysis; Accuracy; Covariance matrix; Feature extraction; Principal component analysis; Semantics; Training; Vectors; global feature; image semantic annotation; principal component analysis; visual perception;
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
978-1-4577-0652-3
DOI :
10.1109/ICSMC.2011.6083795