Online supervised hashing

Çakir, Fatih; Bargal, Sarah Adel; Sclaroff, Stan

doi:10.1016/j.cviu.2016.10.009

Cited by 44 publications

(19 citation statements)

References 18 publications

(17 reference statements)

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“…To handle nearest neighbor search in a dynamic database, online hashing methods [6], [7], [8], [9], [10], [11], [12] have attracted a great attention in recent years. They allow their models to accommodate to the new data coming sequentially, without retraining all stored data points.…”

Section: Related Workmentioning

confidence: 99%

“…They allow their models to accommodate to the new data coming sequentially, without retraining all stored data points. Specifically, Online Hashing [6], [7], AdaptHash [10] and Online Supervised Hashing [12] are online supervised hashing methods, requiring label information, which might not be commonly available in many real-world applications. Stream Spectral Binary Coding (SSBC) [8] and Online Sketching Hashing (OSH) [9] are the only two existing online unsupervised hashing methods which do not require labels, where both of them are matrix sketch-based methods to learn to represent the data seen so far by a small sketch.…”

Section: Related Workmentioning

confidence: 99%

“…Some of the popular works such as Composite Quantization (CQ) [22], Sparse Composite Quantization (SQ) [23], Additive Quantization (AQ) [21] and Tree Quantization (TQ) [24] require the codeword maintenance of the old data to update the codewords due to the constraints or structure of their models. Product Quantization (PQ) [13], as one of the most classical MCQ method for fast nearest neighbor search, decomposes the input space Online supervised hashing ( [6], [7], [10], [11], [12])…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, we consider the following problem. Recently, several studies on online hashing [6], [7], [8], [9], [10], [11], [12] show that hashing based ANN approaches can be adapted to the dynamic database environment by updating hash functions accommodating new streaming data and then updating the hash codes of the exiting stored data via the new hash functions. Searching is performed in the Hamming space which is efficient and has low computational cost.…”

Section: Introductionmentioning

confidence: 99%

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Online Product Quantization

Tsang

Zhang

2018

IEEE Trans. Knowl. Data Eng.

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Approximate nearest neighbor (ANN) search has achieved great success in many tasks. However, existing popular methods for ANN search, such as hashing and quantization methods, are designed for static databases only. They cannot handle well the database with data distribution evolving dynamically, due to the high computational effort for retraining the model based on the new database. In this paper, we address the problem by developing an online product quantization (online PQ) model and incrementally updating the quantization codebook that accommodates to the incoming streaming data. Moreover, to further alleviate the issue of large scale computation for the online PQ update, we design two budget constraints for the model to update partial PQ codebook instead of all. We derive a loss bound which guarantees the performance of our online PQ model. Furthermore, we develop an online PQ model over a sliding window with both data insertion and deletion supported, to reflect the real-time behaviour of the data. The experiments demonstrate that our online PQ model is both time-efficient and effective for ANN search in dynamic large scale databases compared with baseline methods and the idea of partial PQ codebook update further reduces the update cost. Index Terms-Online indexing model, product quantization, nearest neighbour search.!

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Online Product Quantization

Tsang

Zhang

2018

IEEE Trans. Knowl. Data Eng.

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“…It has been observed that adaptive hashing methods that learn to hash from data generally outperform data-independent hashing methods such as Locality Sensitive Hashing [4]. In this paper, we focus on a relatively new family of adaptive hashing methods, namely online adaptive hashing methods [1,2,6,11]. These techniques employ online learning in the presence of streaming data, and are appealing due to their low computational complexity and their ability to adapt to changes in the data distribution.…”

Section: Introductionmentioning

confidence: 99%

MIHash: Online Hashing with Mutual Information

Çakir

Bargal

et al. 2017

2017 IEEE International Conference on Computer Vision (ICCV)

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Learning-based hashing methods are widely used for nearest neighbor retrieval, and recently, online hashing methods have demonstrated good performance-complexity trade-offs by learning hash functions from streaming data. In this paper, we first address a key challenge for online hashing: the binary codes for indexed data must be recomputed to keep pace with updates to the hash functions. We propose an efficient quality measure for hash functions, based on an information-theoretic quantity, mutual information, and use it successfully as a criterion to eliminate unnecessary hash table updates. Next, we also show how to optimize the mutual information objective using stochastic gradient descent. We thus develop a novel hashing method, MIHash, that can be used in both online and batch settings. Experiments on image retrieval benchmarks (including a 2.5M image dataset) confirm the effectiveness of our formulation, both in reducing hash table recomputations and in learning high-quality hash functions.

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Online Matrix Factorization Hashing for Large-Scale Image Retrieval

Wang

et al. 2018

Big Data

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Online supervised hashing

Cited by 44 publications

References 18 publications

Online Product Quantization

Online Product Quantization

MIHash: Online Hashing with Mutual Information

Online Matrix Factorization Hashing for Large-Scale Image Retrieval

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