Online Hashing

Huang, Long-Kai; Yang, Qiang; Zheng, Wei‐Shi

doi:10.1109/tnnls.2017.2689242

Cited by 70 publications

(111 citation statements)

References 41 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%

“…Several recent endeavors have been made for robust and efficient online hashing, i.e., OKH [12], SketchHash [17], AdaptHash [6], OSH [4], FROSH [2], MIHash [5], HCOH [21,19] and BSODH [20]. Unsupervised online hashing methods, e.g., SketchHash [17] and FROSH [2], consider a sketch of the whole streaming data, which is efficient but lacks in accuracy, as the label information is ignored.…”

Section: Introductionmentioning

confidence: 99%

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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%

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

View full text Add to dashboard Cite

show abstract

“…We compare our method against Locality Sensitive Hashing (LSH) [6], Binary Reconstructive Embedding (BRE) [12], Minimal Loss Hashing (MLH) [13], Supervised Hashing with Kernels [14], Fast Hashing (FastHast) [26], Supervised Hashing with Error Correcting Codes (ECC) [22] and Online Kernel Hashing (OKH) [21]. These methods have shown to outperform earlier hashing techniques such as [10,9,16].…”

Section: Methodsmentioning

confidence: 99%

“…For all experiments we follow the protocol used in [14,16,21]. We consider the Hamming ranking in which instances are ranked based on Hamming distances to the query.…”

Section: Evaluation Protocolmentioning

confidence: 99%