Fast Discrete Cross-modal Hashing With Regressing From Semantic Labels

Liu, Xingbo; Nie, Xiushan; Zeng, Wenjun; Cui, Chaoran; Zhu, Lei; Yin, Yilong

doi:10.1145/3240508.3240683

Cited by 64 publications

(34 citation statements)

References 24 publications

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“…Hashing based visual search has attracted extensive research attention in recent years due to the rapid growth of visual data on the Internet [7,33,8,26,12,13,30,32,25,35,27]. In various scenarios, online hashing has become a hot topic due to the emergence of handling the streaming data, which aims to resolve an online retrieval task by updating the hash functions from sequentially arriving data instances.…”

Section: Introductionmentioning

confidence: 99%

Supervised Online Hashing via Hadamard Codebook Learning

Lin

Hong

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

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Online hashing has attracted extensive research attention when facing streaming data. Most online hashing methods, learning binary codes based on pairwise similarities of training instances, fail to capture the semantic relationship, and suffer from a poor generalization in largescale applications due to large variations. In this paper, we propose to model the similarity distributions between the input data and the hashing codes, upon which a novel supervised online hashing method, dubbed as Similarity Distribution based Online Hashing (SDOH), is proposed, to keep the intrinsic semantic relationship in the produced Hamming space. Specifically, we first transform the discrete similarity matrix into a probability matrix via a Gaussianbased normalization to address the extremely imbalanced distribution issue. And then, we introduce a scaling Student t-distribution to solve the challenging initialization problem, and efficiently bridge the gap between the known and unknown distributions. Lastly, we align the two distributions via minimizing the Kullback-Leibler divergence (KL-diverence) with stochastic gradient descent (SGD), by which an intuitive similarity constraint is imposed to update hashing model on the new streaming data with a powerful generalizing ability to the past data. Extensive experiments on three widely-used benchmarks validate the superiority of the proposed SDOH over the state-of-the-art methods in the online retrieval task.

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Section: Introductionmentioning

confidence: 99%

Supervised Online Hashing via Hadamard Codebook Learning

Lin

Hong

et al. 2018

Proceedings of the 26th ACM International Conference on Multimedia

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show abstract

“…However, one common limitation of them is that they relax the discrete constraints within the optimization resulting in suboptimal binary codes. Therefore, a number of cross-modal hashing methods based on discrete optimization are studied [7], [8], [21]. In [22], Locally Linear Embedding is used to extract the manifold information as similarity matrix for learning unified hash codes where the binary codes are learned directly without relaxation.…”

Section: Related Workmentioning

confidence: 99%

“…Different from [7], we further regress the class semantic embeddings A to enhance the semantics of hash codes. In contrast to most of existing methods that regress the hash codes to class labels, we inversly regress the class semantic embeddings to Hamming space to re-align the hash codes and improve discrimination, which has been proved more stable than the former [42].…”

Section: Overall Objective Functionmentioning

confidence: 99%

“…A large number of cross-modal hashing methods have been proposed recently, which consist of shallow learning [7]- [10] and deep learning methods [11]- [13]. All these methods have made significant efforts on improving the performance of cross-modal retrieval.…”

Section: Introductionmentioning

confidence: 99%

“…It is worth noting that the above methods generate binary codes by relaxing the discrete constraints, which leads to a large quantization error. To address this issue, many studies propose to learn binary codes with discrete optimization [7], [20], [21]. For instance, Discrete cross-modal hashing (DCH) [5] was proposed where binary codes are directly learned without relaxation, and label information is used to enhance the discrimination of binary codes through linear classifiers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MESH: A Flexible Manifold-Embedded Semantic Hashing for Cross-Modal Retrieval

et al. 2020

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Hashing based methods for cross-modal retrieval has been widely explored in recent years. However, most of them mainly focus on the preservation of neighborhood relationship and label consistency, while ignore the proximity of neighbors and proximity of classes, which degrades the discrimination of hash codes. And most of them learn hash codes and hashing functions simultaneously, which limits the flexibility of algorithms. To address these issues, in this paper, we propose a two-step cross-modal retrieval method named Manifold-Embedded Semantic Hashing (MESH). It exploits Local Linear Embedding to model the neighborhood proximity and uses class semantic embeddings to consider the proximity of classes. By so doing, MESH can not only extract the manifold structure in different modalities, but also can embed the class semantic information into hash codes to further improve the discrimination of learned hash codes. Moreover, the two-step scheme makes MESH flexible to various hashing functions. Extensive experimental results on three datasets show that MESH is superior to 10 state-of-the-art cross-modal hashing methods. Moreover, MESH also demonstrates superiority on deep features compared with the deep cross-modal hashing method.

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Self-auxiliary Hashing for Unsupervised Cross Modal Retrieval

et al. 2022

Computer Supported Cooperative Work and Social Computing

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Fast Discrete Cross-modal Hashing With Regressing From Semantic Labels

Cited by 64 publications

References 24 publications

Supervised Online Hashing via Hadamard Codebook Learning

Supervised Online Hashing via Hadamard Codebook Learning

MESH: A Flexible Manifold-Embedded Semantic Hashing for Cross-Modal Retrieval

Self-auxiliary Hashing for Unsupervised Cross Modal Retrieval

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