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Frustratingly Easy Cross-Modal Hashing
Dekui Ma1; Jian Liang2,4; Xiangwei Kong1; Ran He(赫然)2,3; Liang, Jian
Conference NameProceedings of the 2016 ACM on Multimedia Conference
Conference DateOctober 15 - 19, 2016
Conference PlaceAmsterdam, The Netherlands
AbstractCross-modal hashing has attracted considerable attention due to its low storage cost and fast retrieval speed. Recently, more and more sophisticated researches related to this topic are proposed. However, they seem to be inefficient computationally for several reasons. On one hand, learning coupled hash projections makes the iterative optimization problem challenging. On the other hand, individual collective binary codes for each content are also learned with a high computation complexity. In this paper we describe a simple yet effective cross-modal hashing approach that can be implemented in just three lines of code. This approach first obtains the binary codes for one modality via unimodal hashing methods (e.g., iterative quantization (ITQ)), then applies simple linear regression to project the other modalities into the obtained binary subspace. Obviously, it is non-iterative and parameter-free, which makes it more attractive for many real-world applications. We further compare our approach with other state-of-the-art methods on four benchmark datasets (i.e., the Wiki, VOC, LabelMe and NUS-WIDE datasets). Despite its extraordinary simplicity, our approach performs remarkably and generally well for these datasets under different experimental settings (i.e., large-scale, high-dimensional and multi-label datasets).
KeywordCross-modal Hashing
Indexed ByEI
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Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Corresponding AuthorLiang, Jian
Affiliation1.School of Information and Communication Engineering, Dalian University of Technology
2.Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR), CASIA
3.CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT)
4.University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Dekui Ma,Jian Liang,Xiangwei Kong,et al. Frustratingly Easy Cross-Modal Hashing[C],2016.
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