CASIA OpenIR  > 智能感知与计算研究中心
Online Codebook Reweighting Using Pairwise Constraints for Image Classification
Xin Zhao; Jianwei Ding; Kaiqi Huang; Tieniu Tan
2011
Conference NameThe First Asian Conference on Pattern Recognition
Source PublicationPattern Recognition, 2011
Pages662-666
Conference Date2011
Conference PlaceBeijing, China
AbstractBag-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.
KeywordClutter   image Classification   image Coding
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12695
Collection智能感知与计算研究中心
Corresponding AuthorKaiqi Huang
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Xin Zhao,Jianwei Ding,Kaiqi Huang,et al. Online Codebook Reweighting Using Pairwise Constraints for Image Classification[C],2011:662-666.
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