Structure Sensitive Hashing With Adaptive Product Quantization

Liu, Xianglong; Du, Bowen; Deng, Cheng; Liu, Ming; Liu, Bo

doi:10.1109/tcyb.2015.2474742

Cited by 69 publications

(19 citation statements)

References 44 publications

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“…Recently, the amount of literatures have grown up considerably around the theme of hashing [12,13,32,45,45,46]. According to whether supervised information are involved in the learning phase, existing hashing models can be divided into two categories: supervised hashing methods and unsupervised hashing methods.…”

Section: Related Workmentioning

confidence: 99%

DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs

Yang

Liu

Deng

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Self Cite

139

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Due to the high storage and search efficiency, hashing has become prevalent for large-scale similarity search. Particularly, deep hashing methods have greatly improved the search performance under supervised scenarios. In contrast, unsupervised deep hashing models can hardly achieve satisfactory performance due to the lack of reliable supervisory similarity signals. To address this issue, we propose a novel deep unsupervised hashing model, dubbed Distill-Hash, which can learn a distilled data set consisted of data pairs, which have confidence similarity signals. Specifically, we investigate the relationship between the initial noisy similarity signals learned from local structures and the semantic similarity labels assigned by a Bayes optimal classifier. We show that under a mild assumption, some data pairs, of which labels are consistent with those assigned by the Bayes optimal classifier, can be potentially distilled. Inspired by this fact, we design a simple yet effective strategy to distill data pairs automatically and further adopt a Bayesian learning framework to learn hash functions from the distilled data set. Extensive experimental results on three widely used benchmark datasets show that the proposed DistillHash consistently accomplishes the stateof-the-art search performance.

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Section: Related Workmentioning

confidence: 99%

DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs

Yang

Liu

Deng

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Self Cite

139

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“…Unsupervised hashing methods aim to preserve the linkages among the unlabeled training data points. Typical examples include graph based hashing [10,36,37], minimize quantization error [38], and minimize reconstruction error [9,11,[33][34][35]. Supervised methods utilize the semantic labels or relevance information to improve the quality of hash codes.…”

Section: Related Workmentioning

confidence: 99%

Asymmetric Deep Semantic Quantization for Image Retrieval

et al. 2019

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Due to its fast retrieval and storage efficiency capabilities, hashing has been widely used in nearest neighbor retrieval tasks. By using deep learning based techniques, hashing can outperform non-learning based hashing technique in many applications. However, we argue that the current deep learning based hashing methods ignore some critical problems (e.g., the learned hash codes are not discriminative due to the hashing methods being unable to discover rich semantic information and the training strategy having difficulty optimizing the discrete binary codes). In this paper, we propose a novel image hashing method, termed as Asymmetric Deep Semantic Quantization (ADSQ). ADSQ is implemented using three stream frameworks, which consist of one LabelNet and two ImgNets. The LabelNet leverages the power of three fully-connected layers, which are used to capture rich semantic information between image pairs. For the two ImgNets, they each adopt the same convolutional neural network structure, but with different weights (i.e., asymmetric convolutional neural networks). The two ImgNets are used to generate discriminative compact hash codes. Specifically, the function of the LabelNet is to capture rich semantic information that is used to guide the two ImgNets in minimizing the gap between the real-continuous features and the discrete binary codes. Furthermore, ADSQ can utilize the most critical semantic information to guide the feature learning process and consider the consistency of the common semantic space and Hamming space. Experimental results on three benchmarks (i.e., CIFAR-10, NUS-WIDE, and ImageNet) demonstrate that the proposed ADSQ can outperforms current state-of-the-art methods.

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“…Quantization that requires codewords maintenance: AQ, CQ, SQ, TQ, KMH, ABQ, SSH ( [21], [22], [23], [24], [25], [26], [27], [28] into a Cartesian product of subspaces. The codeword of a data instance is represented by the concatenation of the subcodeword of the data in all subspaces.…”

Section: Related Workmentioning

confidence: 99%

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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Structure Sensitive Hashing With Adaptive Product Quantization

Cited by 69 publications

References 44 publications

DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs

DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs

Asymmetric Deep Semantic Quantization for Image Retrieval

Online Product Quantization

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