CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Online Sketching Hashing
Leng, Cong1; Wu, Jiaxiang1; Cheng, Jian1; Bai, Xiao2; Lu, Hanqing1
2015
Conference NameCVPR2015
Source PublicationIEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Conference Date2015
Conference PlaceUSA
Abstract
Recently, hashing based approximate nearest neighbor (ANN) search has attracted much attention. Extensive new algorithms have been developed and successfully applied to
different applications. However, two critical problems are rarely mentioned. First, in real-world applications, the data often comes in a streaming fashion but most of existing hashing methods are batch based models. Second, when the dataset becomes huge, it is almost impossible to load all the data into memory to train hashing models. In this paper, we propose a novel approach to handle these two problems simultaneously based on the idea of data sketching. A sketch of one dataset preserves its major characters but with significantly smaller size. With a small size sketch, our method can learn hash functions in an online fashion, while needs rather low computational complexity and storage space.
Extensive experiments on two large scale benchmarks and one synthetic dataset demonstrate the efficacy of the proposed method.
KeywordSketching
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11785
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorCheng, Jian
Affiliation1.中科院自动化研究所
2.北京航空航天大学
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,Wu, Jiaxiang,Cheng, Jian,et al. Online Sketching Hashing[C],2015.
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