CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Supervised Hashing with Soft Constraints
Leng, Cong; Cheng, Jian; Wu, Jiaxiang; Zhang, Xi; Lu, Hanqing
2014
Conference Namethe 23rd ACM International Conference on Conference on Information and Knowledge Management
Source PublicationInternational Conference on Information and Knowledge Managemen
Pages1851-1854
Conference Date2014
Conference PlaceChina
Abstract
Due to the ability to preserve semantic similarity in Hamming space, supervised hashing has been extensively studied recently. Most existing approaches encourage two dissimilar samples to have maximum Hamming distance. This may lead to an unexpected consequence that two unnecessarily similar samples would have the same code if they are both dissimilar with another sample. Besides, in existing methods, all labeled pairs are treated with equal importance without considering the semantic gap, which is not conducive to thoroughly leverage the supervised information. We present a general framework for supervised hashing to address the above two limitations. We do not toughly require a dissimilar pair to have maximum Hamming distance. Instead, a soft constraint which can be viewed as a regularization to avoid over-fitting is utilized. Moreover, we impose different weights to different training pairs, and these weights can be automatically adjusted in the learning process. Experiments on two benchmarks show that the proposed method can easily outperform other state-of-the-art methods.
KeywordSupervised Hashing Soft Constraints Weights Boosting
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/4704
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorCheng, Jian
Affiliation中科院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Leng, Cong,Cheng, Jian,Wu, Jiaxiang,et al. Supervised Hashing with Soft Constraints[C],2014:1851-1854.
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