Online Codebook Reweighting Using Pairwise Constraints for Image Classification

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	Online Codebook Reweighting Using Pairwise Constraints for Image Classification
	Xin Zhao; Jianwei Ding; Kaiqi Huang; Tieniu Tan
	2011
会议日期	2011
会议地点	Beijing, China
关键词	Clutter image Classification image Coding
页码	662-666
英文摘要	Bag-of-words (BoW) model is widely used for image classification. Recently, the framework of sparse coding and max pooling proved an effective approach for image classification. Max pooling adopts a winner-take-all strategy. Thus, it can be regarded as a codebook weighting process. The results of this process are the weights of the associated codebook. However, there are high intra-class variations and strong background clutters in many image classification tasks. The weights obtained by max pooling only have limited information. This paper presents a codebook reweighting algorithm using pairwise constraints to improve the performance of sparse coding and max pooling framework. Pairwise constraints are the natural way of encoding the relationships between pairs of images. Therefore, the reweighted codebook is more effective to describe the relevance between pairs of images. An efficient online learning algorithm is presented based on passive-aggressive training strategy. We compare our method with other state-of-the-art methods on Graz-01 & 02 datasets. Experimental results illustrate the effectiveness and efficiency of our method for image classification.
会议录	Pattern Recognition, 2011
语种	英语
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/12695]
专题	自动化研究所_智能感知与计算研究中心
通讯作者	Kaiqi Huang
作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Xin Zhao,Jianwei Ding,Kaiqi Huang,et al. Online Codebook Reweighting Using Pairwise Constraints for Image Classification[C]. 见:. Beijing, China. 2011.