Learning to Hash With Optimized Anchor Embedding for Scalable Retrieval
Abstract
Sparse representation and image hashing are powerful tools for data representation and image retrieval respectively. The combinations of these two tools for scalable image retrieval, i.e., sparse hashing (SH) methods, have been proposed in recent years and the preliminary results are promising. The core of those methods is a scheme that can efficiently embed the (high-dimensional) image features into a low-dimensional Hamming space, while preserving the similarity between features. Existing SH methods mostly focus on finding better sparse representations of images in the hash space. We argue that the anchor set utilized in sparse representation is also crucial, which was unfortunately underestimated by the prior art. To this end, we propose a novel SH method that optimizes the integration of the anchors, such that the features can be better embedded and binarized, termed as Sparse Hashing with Optimized Anchor Embedding. The central idea is to push the anchors far from the axis while preserving their relative positions so as to generate similar hashcodes for neighboring features. We formulate this idea as an orthogonality constrained maximization problem and an efficient and novel optimization framework is systematically exploited. Extensive experiments on five benchmark image data sets demonstrate that our method outperforms several state-of-the-art related methods.
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Although bit-independent constraint increases information content, quantization errors accumulate during iterative optimization and imbalanced variance within each dimension remains an open issue affecting hash code quality critically.
“Although the information content is increased inevitably in this case, the quantization errors are accumulated during the iterative optimization and the aforementioned imbalanced variance within each dimension is still an open issue that affects the hash code quality critically.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [48]
ActivityNet's unpleasant performance is reasonably explained by too many untrimmed videos with enormous intra-class variance, which lowers hash code quality and makes retrieval more intractable.
“A reasonable explanation for the unpleasant performance is that ActivityNet contains too many untrimmed videos with enormous intra-class variance, which lowers the hash code quality heavily and makes the retrieval more intractable [26], [48] compared to the experiments on FCVID and YFCC.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [48]
The mAP value at 64 bits of UDVH-TSN is 22.1%, which is 7.3% higher than the 14.8% reported for NSH.
“For example, the mAP value at 64 bits of UDVH-TSN is 22.1%, which is 7.3% higher than 14.8% as reported in their paper of NSH.”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [48]
The binary code B generated by balanced rotation is used iteratively to guide deep hash function learning, and B is expected to preserve local structures and balanced properties from feature embedding and balanced rotation.
“The core idea behind such self-taught frameworks is that the binary code B generated by balanced rotation is utilized iteratively to guide the deep hash function learning, where B is expected to preserve the local structures and balanced properties from the processes of feature embedding and balanced rotation, respectively [48].”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [48]
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Self-citationDiscovering and Distinguishing Multiple Visual Senses for Polysemous Words
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Complementary Incremental Hashing With Query-Adaptive Re-Ranking for Image Retrieval
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Secure and privacy-preserving data sharing in the cloud based on lossless image coding
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Self-citationIncremental Hash-Bit Learning for Semantic Image Retrieval in Nonstationary Environments
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Supervised discrete cross-modal hashing based on kernel discriminant analysis
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Learning a maximized shared latent factor for cross-modal hashing
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Hadamard Coding for Supervised Discrete Hashing
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Few-shot decision tree for diagnosis of ultrasound breast tumor using BI-RADS features
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Concept Preserving Hashing for Semantic Image Retrieval With Concept Drift
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Local Semantic-Aware Deep Hashing With Hamming-Isometric Quantization
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Video hashing with secondary frames and invariant moments
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Self-citationLearnable Central Similarity Quantization for Efficient Image and Video Retrieval
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On Trivial Solution and High Correlation Problems in Deep Supervised Hashing
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Self-citationUnsupervised Multi-modal Hashing for Cross-Modal Retrieval
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Transformer-Based Deep Hashing Method for Multi-Scale Feature Fusion
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Discriminative and Orthogonal Subspace Constraints-Based Nonnegative Matrix Factorization
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A new weakly supervised discrete discriminant hashing for robust data representation
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Siamese Dilated Inception Hashing With Intra-Group Correlation Enhancement for Image Retrieval
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Discriminative correlation hashing for supervised cross-modal retrieval
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Minimizing Reconstruction Bias Hashing via Joint Projection Learning and Quantization
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Hyperspectral image classification based on joint spectrum of spatial space and spectral space
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Unsupervised hashing based on the recovery of subspace structures
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A survey of real-time approximate nearest neighbor query over streaming data for fog computing
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Codebook-Free Compact Descriptor for Scalable Visual Search
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Deep top similarity hashing with class-wise loss for multi-label image retrieval
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Deep Weibull hashing with maximum mean discrepancy quantization for image retrieval
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Local Embedding Learning via Landmark-Based Dynamic Connections
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Review on Image Representation Compression and Retrieval Approaches
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Deep Neighbor Discriminant Binary Embedding for Multi-Label Image Retrieval
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Deep Video Hashing Using 3DCNN with BERT
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Joint structural similarity and entropy estimation for coded-exposure image restoration
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Aesthetics-Guided Graph Clustering With Absent Modalities Imputation
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Self-citationOptimized projection for hashing
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Self-citationFast algorithm for anchor graph hashing
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MambaHash: Visual State Space Deep Hashing Model for Large-Scale Image Retrieval
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- Authors
- Guo, Y., Ding, G., Liu, L., Han, J., & Shao, L.
- Affiliations
- Tsinghua University (CN)
- Northumbria University (GB)
- University of East Anglia (GB)
- Venue
- IEEE Transactions on Image Processing
- Published
- 2017-03
- Volume
- 26
- Issue
- 3
- Pages
- 1344-1354
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2572852038
- Cited by
- 108
- References
- 70
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 2 · 2023: 7 · 2022: 7 · 2021: 10 · 2020: 7 · 2019: 14 · 2018: 38 · 2017: 18
- FWCI
- 8.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (publishedVersion)
- Northumbria Research Link (Northumbria University) (acceptedVersion)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61571269, 61271394)
- National Basic Research Project of China (2015CB352300)
- Royal Society Newton Mobility (IE150997)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- image hashing, image retrieval, sparse representation, Hamming space, orthogonality constraints, binarization, feature embedding
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