Codebook reconstruction with holistic information fusion

CASIA OpenIR > 智能感知与计算研究中心

	Codebook reconstruction with holistic information fusion
	Yuhang Zhao; Zhaoxiang Zhang; Yunhong Wang
发表期刊	IET Computer Vision
	2013-01-03
卷号	6 期号:6 页码:626-634
摘要	Bag of feature model has been shown to be one of the most successful methods in generic image categorisation. However, creating codebook by clustering local feature vectors (e.g. Kmeans) may lose holistic information of images. This study presents a novel process called `Correlation Feedback` for codebook construction. It introduces semantic similarities of words by measuring correlations among distribution of them within one image. Furthermore, the authors employ label propagation process to spread the affinities among all features. An enhanced codebook is constructed based on fusion of the new similarity matrix with locality preserving projection, which is a linear manifold learning algorithm that can be expanded on both training and testing samples. Experimental results on 15 different scenes and ImageNet show promising performance of importing the novel similarity to dictionary construction.
关键词	Pattern Clustering Feature Extraction Image Classification Image Fusion
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/13222
专题	智能感知与计算研究中心
通讯作者	Zhaoxiang Zhang
推荐引用方式 GB/T 7714	Yuhang Zhao,Zhaoxiang Zhang,Yunhong Wang. Codebook reconstruction with holistic information fusion[J]. IET Computer Vision,2013,6(6):626-634.
APA	Yuhang Zhao,Zhaoxiang Zhang,&Yunhong Wang.(2013).Codebook reconstruction with holistic information fusion.IET Computer Vision,6(6),626-634.
MLA	Yuhang Zhao,et al."Codebook reconstruction with holistic information fusion".IET Computer Vision 6.6(2013):626-634.