Hashing with Angular Reconstructive Embeddings

Hu, Mengqiu; Yang, Yang; Shen, Fumin; Xie, Ning; Shen, Heng Tao

doi:10.1109/tip.2017.2749147

Cited by 94 publications

(33 citation statements)

References 49 publications

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“…Similarly, the ordinal relation preserving restriction for bitwise weights is re-defined as in Eq. (9).…”

Section: S(i J)mentioning

confidence: 99%

“…The data-independent hashing, such as locality-sensitive hashing (LSH) [7], randomly generates hashing functions, *Correspondence: zhwang@sdut.edu.cn 1 School of Computer Science and Technology, Shandong University of Technology, Zibo, 255000 China Full list of author information is available at the end of the article and it typically requires a long binary code or multihash tables to achieve satisfying performance. In contrast, the data-dependent hashing algorithms, such as BDMFH [8] and ARE [9], utilize machine learning mechanisms to learn similarity preserving binary codes. Bidirectional discrete matrix factorization hashing (BDMFH) [8] proposes to alternate two mutually promoted processes of learning binary codes from data and recovering data from the binary codes.…”

Section: Introductionmentioning

confidence: 99%

“…To enforce the learned binary codes inheriting intrinsic structure from the original data, BDMFH designs an inverse factorization model. Angular reconstructive embeddings (ARE) method [9] learns binary codes by minimizing the reconstruction error between the cosine similarities computed by the original data and the binary embeddings. Usually, the data-dependent hashing can obtain an excellent approximate nearest neighbors (ANN) search performance with compact binary codes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Minimal residual ordinal loss hashing with an adaptive optimization mechanism

Wang

Sun

Zhang

et al. 2020

J Image Video Proc.

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The binary coding technique has been widely used in approximate nearest neighbors (ANN) search tasks. Traditional hashing algorithms treat binary bits equally, which usually causes an ambiguous ranking. To solve this issue, we propose an innovative bitwise weight method dubbed minimal residual ordinal loss hashing (MROLH). Different from a two-step mechanism, MROLH simultaneously learns binary codes and bitwise weights by a feedback mechanism. When the algorithm converges, the binary codes and bitwise weights can be well adaptive to each other. Furthermore, we establish the ordinal relation preserving constraint based on quartic samples to enhance the power of preserving relative similarity. To decrease the training complexity, we utilize a tensor ordinal graph to represent quartic ordinal relation, and the original objective function is approximated by the one based on triplet samples. In this paper, we also assign different weight values to training samples. During the training procedure, the weight of each data is initialized to the same value, and we iteratively boost the weight of the data whose relative similarity is not well preserved. As a result, we can minimize the residual ordinal loss. Experimental results on three large-scale ANN search benchmark datasets, i.e., SIFT1M, GIST1M, and Cifar10, show that the proposed method MROLH achieves a superior ANN search performance in both the Hamming space and the weighted Hamming space over the sate-of-the-art approaches.

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“…Similarly, the ordinal relation preserving restriction for bitwise weights is re-defined as in Eq. (9).…”

Section: S(i J)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Minimal residual ordinal loss hashing with an adaptive optimization mechanism

Wang

Sun

Zhang

et al. 2020

J Image Video Proc.

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show abstract

“…Finally, the algorithm to learn parameters should be fast and for unseen samples, the hashing method should produce the hash codes efficiently. It is very challenging to simultaneously satisfy all three requirements, especially, under the binary constraint which leads to an NP-hard mixed-integer optimization The proposed hashing methods in literature can be categorized into data-independence (Gionis et al (1999); Kulis and Grauman (2009); Raginsky and Lazebnik (2009)) and datadependence; in which, the latter recently receives more attention in both (semi-)supervised (Do et al (2016b); Kulis and Darrell (2009) ;Lin et al (2014); Liu et al (2012); Norouzi et al (2012); Shen et al (2015); ; Cao et al (2018); Jain et al (2017); Liu et al (2016); Lin et al (2015); Lai et al (2015); Lin et al (2016)) and unsupervised (Carreira-Perpiñán and Raziperchikolaei (2015); Do et al (2016aDo et al ( , 2017; Gong and Lazebnik (2011); He et al (2013); Heo et al (2012); Shen et al (2018); Hu et al (2018); Huang and Lin (2018); y. Duan et al (2018); Wang et al (2018); Duan et al (2017); En et al (2017); ) manners.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous compression and quantization: A joint approach for efficient unsupervised hashing

Hoang

Toan

et al. 2020

Computer Vision and Image Understanding

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For unsupervised data-dependent hashing, the two most important requirements are to preserve similarity in the low-dimensional feature space and to minimize the binary quantization loss. A well-established hashing approach is Iterative Quantization (ITQ), which addresses these two requirements in separate steps. In this paper, we revisit the ITQ approach and propose novel formulations and algorithms to the problem. Specifically, we propose a novel approach, named Simultaneous Compression and Quantization (SCQ), to jointly learn to compress (reduce dimensionality) and binarize input data in a single formulation under strict orthogonal constraint. With this approach, we introduce a loss function and its relaxed version, termed Orthonormal Encoder (OnE) and Orthogonal Encoder (OgE) respectively, which involve challenging binary and orthogonal constraints. We propose to attack the optimization using novel algorithms based on recent advance in cyclic coordinate descent approach. Comprehensive experiments on unsupervised image retrieval demonstrate that our proposed methods consistently outperform other state-of-the-art hashing methods. Notably, our proposed methods outperform recent deep neural networks and GAN based hashing in accuracy, while being very computationally-efficient.

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“…A solution to avoid an exhaustive comparison is to employ approximate NN (ANN) search algorithms. ANN algorithms aim at identifying images from a large archive that have a high probability to be NNs of the query image with a sublinear, or even constant time complexity [1]. Recently, hashing-based ANN search techniques have been used in RS due to their high time-efficient (in terms of both storage and speed) and accurate search capability within huge data archives [2].…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Multicode Hashing Method for Accurate and Scalable Remote Sensing Image Retrieval

Reato

Demir

Bruzzone

2019

IEEE Geosci. Remote Sensing Lett.

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Hashing methods have recently attracted great attention for approximate nearest neighbor search in massive remote sensing (RS) image archives due to their computational and storage effectiveness. The existing hashing methods in RS represent each image with a single-hash code that is usually obtained by applying hash functions to global image representations. Such an approach may not optimally represent the complex information content of RS images. To overcome this problem, in this letter, we present a simple yet effective unsupervised method that represents each image with primitive-cluster sensitive multi-hash codes (each of which corresponds to a primitive present in the image). To this end, the proposed method consists of two main steps: 1) characterization of images by descriptors of primitive-sensitive clusters and 2) definition of multi-hash codes from the descriptors of the primitive-sensitive clusters. After obtaining multi-hash codes for each image, retrieval of images is achieved based on a multi-hash-code-matching scheme. Any hashing method that provides single-hash code can be embedded within the proposed method to provide primitive-sensitive multihash codes. Compared with state-of-the-art single-code hashing methods in RS, the proposed method achieves higher retrieval accuracy under the same retrieval time, and thus it is more efficient for operational applications.

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Hashing with Angular Reconstructive Embeddings

Cited by 94 publications

References 49 publications

Minimal residual ordinal loss hashing with an adaptive optimization mechanism

Minimal residual ordinal loss hashing with an adaptive optimization mechanism

Simultaneous compression and quantization: A joint approach for efficient unsupervised hashing

An Unsupervised Multicode Hashing Method for Accurate and Scalable Remote Sensing Image Retrieval

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