Asymmetric Deep Semantic Quantization for Image Retrieval

Yang, Zhan; Raymond, Osolo Ian; Sun, Wuqing; Long, Jun

doi:10.1109/access.2019.2920712

Cited by 7 publications

(5 citation statements)

References 35 publications

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“…To solve this limitation, some methods have been proposed to further enrich the semantic information of hash codes in addition to direct semantic supervision, DSEH and AD-SQ [38,39] utilizes self-supervised networks to capture rich pairwise semantic information to guide feature learning network from both semantic level and hash codes level, thus enriching semantic information and pairwise correlation in hash codes. In comparison to [38,39] our work improves the guidance mechanism of self-supervised network on feature learning network to be less time-consuming and more semantic-targeted with asymmetric learning strategy. Moreover, the generated semantic codes are further exploited to identify scalable margins for pairwise contrastive-form constraint for higher-level pairwise correlations and highly-discriminative hash code representations.…”

Section: Deep Hashing Methodsmentioning

confidence: 99%

“…Asymmetric hashing methods [3,19,39,40] have recently becoming an eye-catching research focus. In ADSH [19], query points and database points are treated asymmetrically, with only query points being engaged in the stage of updating deep network parameters, while the hash codes for database are directly learned as independent parameters, the hash cod-es generated by query and database are correlated through asymmetric pairwise constraints, such that the dataset points can be efficiently utilized during hashing function learning procedure.…”

Section: Asymmetric Hashing Methodsmentioning

confidence: 99%

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Self-supervised asymmetric deep hashing with margin-scalable constraint

Yu,

Wu,

Dou

et al. 2020

Preprint

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Due to its validity and rapidity, image retrieval based on deep hashing approaches is widely concerned especially in large-scale visual search. However, many existing deep hashing methods inadequately utilize label information as guidance of feature learning network without more advanced exploration in semantic space, besides the similarity correlations in hamming space are not fully discovered and embedded into hash codes, by which the retrieval quality is diminished with inefficient preservation of pairwise correlations and multi-label semantics. To cope with these problems, we propose a novel self-supervised asymmetric deep hashing with margin-scalable constraint(SADH) approach for image retrieval. SADH implements a self-supervised network to preserve supreme semantic information in a semantic feature map and a semantic code map for each semantics of the given dataset, which efficiently-and-precisely guides a feature learning network to preserve multi-label semantic information with asymmetric learning strategy. Moreover, for the feature learning part, by further exploiting semantic maps, a new margin-scalable constraint is employed for both highly-accurate construction of pairwise correlation in the hamming space and more discriminative hash code representation. Extensive empirical research on three benchmark datasets validate that the proposed method outperforms several state-of-the-art approaches.

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Section: Deep Hashing Methodsmentioning

confidence: 99%

Section: Asymmetric Hashing Methodsmentioning

confidence: 99%

Self-supervised asymmetric deep hashing with margin-scalable constraint

Yu,

Wu,

Dou

et al. 2020

Preprint

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“…x < 0.5 (7) where 𝑆𝑆 𝑖𝑖 is the hash-like code obtained by passing the input image i through the hash layer. To minimize the quantization error arising during the conversion of the true feature representation into a binary hash code [42]- [44], we introduce a quantization loss function to bring the binary code closer to the desired hash code:…”

Section: B Hash Learningmentioning

confidence: 99%

ICIHRN: An Interpretable Multilabel Hash Retrieval Method for Satellite Cloud Images

Jin,

Cai,

Pan

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Observing clouds to understand the weather is a crucial method for people to forecast upcoming conditions. Utilizing content-based satellite cloud image retrieval allows for the swift discovery of comparable historical cloud images, significantly aiding meteorologists in their advanced investigations. Nevertheless, satellite cloud images often present complexities due to their inclusion of diverse cloud types, leading to inadequate retrieval outcomes when relying on conventionally employed single-label retrieval techniques. Despite notable accomplishments in cloud image retrieval applications utilizing deep neural networks, concerns regarding network interpretability undermine confidence in the model's deductive outcomes. This paper introduces the interpretable cloud image hash retrieval network (ICIHRN), a framework that employs a singular object-level global unit alongside multiple local feature units for the purpose of generating hash codes tailored to cloud image retrieval. Furthermore, an attention branching network is incorporated to enhance the model's focus on discriminative regions within the image. Additionally, a suppression module is implemented to progressively uncover complementary regions through the suppression of prominent areas in preceding layers and the amalgamation of relationships among activated regions. This ensures that each feature unit is endowed with distinctive semantic information, thereby imparting a level of interpretability to the retrieval outcomes. On this foundation, multi-label supervision is seamlessly integrated into the deep hash learning framework. This integration not only enhances the depiction of intricate semantic contents within cloud images but also boosts retrieval efficiency. Comprehensive experimental outcomes, grounded in the publicly accessible satellite cloud map dataset LSCIDMR-V2, demonstrate superior performance relative to other methods.

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“…Asymmetric Deep Semantic Quantization [127] increases the performance by utilizing two ImgNets in minimizing the gap between the real-continuous features and the discrete binary codes, and the difference loss is also added.…”

Section: =mentioning

confidence: 99%

A Survey on Deep Hashing Methods

Luo¹,

Wang²,

Wu³

et al. 2020

Preprint

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Nearest neighbor search is to find the data points in the database such that the distances from them to the query are the smallest, which is a fundamental problem in various domains, such as computer vision, recommendation systems and machine learning. Hashing is one of the most widely used method for its computational and storage efficiency. With the development of deep learning, deep hashing methods show more advantages than traditional methods. In this paper, we present a comprehensive survey of the deep hashing algorithms. Based on the loss function, we categorize deep supervised hashing methods according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise similarity preserving, implicit similarity preserving, as well as quantization.In addition, we also introduce some other topics such as deep unsupervised hashing and multi-modal deep hashing methods. Meanwhile, we also present some com-

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Asymmetric Deep Semantic Quantization for Image Retrieval

Cited by 7 publications

References 35 publications

Self-supervised asymmetric deep hashing with margin-scalable constraint

Self-supervised asymmetric deep hashing with margin-scalable constraint

ICIHRN: An Interpretable Multilabel Hash Retrieval Method for Satellite Cloud Images

A Survey on Deep Hashing Methods

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