Global Hashing System for Fast Image Search

Tian, Dayong; Tao, Dacheng

doi:10.1109/tip.2016.2617081

Cited by 14 publications

(8 citation statements)

References 45 publications

(43 reference statements)

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“…Spectral hashing (SH) [5], one of the most popular and pioneering data-dependent unimodal hashing methods, generate hashing codes by solving a relaxed mathematical problem to avoid computing the affinity matrix that requires calculating and storing pairwise distances of the whole data set [20]. The authors argued that two constraints for a good code matrix are orthogonality and balance, either of which leads to an NP-hard problem.…”

Section: A Unimodal Hashingmentioning

confidence: 99%

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Learning Decorrelated Hashing Codes With Label Relaxation for Multimodal Retrieval

2020

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Due to the correlation among hashing bits, the retrieval performance improvement becomes slower when the hashing code length becomes longer. Existing methods try to regularize the projection matrix as an orthogonal matrix to decorrelate hashing codes. However, the binarization of projected data may completely break the orthogonality. In this paper, we propose a minimum correlation regularization (MCR) for multimodal hashing. Rather than being imposed on projection matrix, MCR is imposed on a differentiable function which approximates the binarization. On the other hand, binary labels could not precisely reflect the distances among data. Hence, we propose a label relaxation scheme to achieve better performance.

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Section: A Unimodal Hashingmentioning

confidence: 99%

“…All aforementioned unimodal hashing models cannot generate balanced code matrix. Spherical hashing (SpH) [27] and global hashing system (GHS) [20] quantize the distance between a data point and a special point. The closer half to a special point is denoted as 1 while the further half is denoted as 0.…”

Section: A Unimodal Hashingmentioning

confidence: 99%

Learning Decorrelated Hashing Codes With Label Relaxation for Multimodal Retrieval

2020

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“…Spectral hashing (SH) [27], one of the most popular and pioneering data-dependent unimodal hashing methods, generate hashing codes by solving a relaxed mathematical problem to avoid computing the affinity matrix that requires calculating and storing pairwise distances of the whole data set [23]. The authors argued that two constraints for a good code matrix are orthogonality and balance, either of which leads to an NP-hard problem.…”

Section: A Unimodal Hashingmentioning

confidence: 99%

“…All aforementioned unimodal hashing models cannot generate balanced code matrix. Spherical hashing (SpH) [9] and global hashing system (GHS) [23] quantize the distance between a data point and a special point. The closer half to a special point is denoted as 1 while the further half is denoted as 0.…”

Section: A Unimodal Hashingmentioning

confidence: 99%

Learning Decorrelated Hashing Codes for Multimodal Retrieval

Tian

2018

Preprint

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In social networks, heterogeneous multimedia data correlate to each other, such as videos and their corresponding tags in YouTube and image-text pairs in Facebook. Nearest neighbor retrieval across multiple modalities on large data sets becomes a hot yet challenging problem. Hashing is expected to be an efficient solution, since it represents data as binary codes. As the bit-wise XOR operations can be fast handled, the retrieval time is greatly reduced. Few existing multimodal hashing methods consider the correlation among hashing bits. The correlation has negative impact on hashing codes. When the hashing code length becomes longer, the retrieval performance improvement becomes slower. In this paper, we propose a minimum correlation regularization (MCR) for multimodal hashing. First, the sigmoid function is used to embed the data matrices. Then, the MCR is applied on the output of sigmoid function. As the output of sigmoid function approximates a binary code matrix, the proposed MCR can efficiently decorrelate the hashing codes. Experiments show the superiority of the proposed method becomes greater as the code length increases.

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“…Unsupervised hashing methods aim to preserve the metric (Euclidean neighbor) structure among the training data. Representative unsupervised hashing methods include spectral hashing (SH) [22], iterative quantization (ITQ) [16], isotropic hashing (IsoHash) [7], spherical hashing (SPH) [13], inductive manifold hashing (IMH) [17], anchor graph hashing (AGH) [23], discrete graph hashing (DGH) [24], latent semantic minimal hashing (LSMH) [25] and global hashing system (GHS) [26]. Due to the semantic gap [27], unsupervised hashing methods usually can not achieve satisfactory retrieval performance in real applications.…”

Section: Introductionmentioning

confidence: 99%

Deep Discrete Supervised Hashing

Jiang

Cui

2018

IEEE Trans. on Image Process.

106

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Hashing has been widely used for large-scale search due to its low storage cost and fast query speed. By using supervised information, supervised hashing can significantly outperform unsupervised hashing. Recently, discrete supervised hashing and feature learning based deep hashing are two representative progresses in supervised hashing. On one hand, hashing is essentially a discrete optimization problem. Hence, utilizing supervised information to directly guide discrete (binary) coding procedure can avoid sub-optimal solution and improve the accuracy. On the other hand, feature learning based deep hashing, which integrates deep feature learning and hash-code learning into an end-to-end architecture, can enhance the feedback between feature learning and hash-code learning. The key in discrete supervised hashing is to adopt supervised information to directly guide the discrete coding procedure in hashing. The key in deep hashing is to adopt the supervised information to directly guide the deep feature learning procedure. However, most deep supervised hashing methods cannot use the supervised information to directly guide both discrete (binary) coding procedure and deep feature learning procedure in the same framework. In this paper, we propose a novel deep hashing method, called deep discrete supervised hashing (DDSH). DDSH is the first deep hashing method which can utilize pairwise supervised information to directly guide both discrete coding procedure and deep feature learning procedure and thus enhance the feedback between these two important procedures. Experiments on four real datasets show that DDSH can outperform other state-of-the-art baselines, including both discrete hashing and deep hashing baselines, for image retrieval.

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Global Hashing System for Fast Image Search

Cited by 14 publications

References 45 publications

Learning Decorrelated Hashing Codes With Label Relaxation for Multimodal Retrieval

Learning Decorrelated Hashing Codes With Label Relaxation for Multimodal Retrieval

Learning Decorrelated Hashing Codes for Multimodal Retrieval

Deep Discrete Supervised Hashing

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