Deep Hash Distillation for Image Retrieval

Jang, Young Kyun; Gu, Geonmo; Ko, Byungsoo; Kang, Isaac; Cho, Nam Ik

doi:10.1007/978-3-031-19781-9_21

Cited by 15 publications

(9 citation statements)

References 38 publications

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“…On the other hand, to fully use several information-theoretic aspects in the information retrieval process, the QSMIH method [22] adopts Quadratic Mutual Information to optimize the learned hash codes, which can better meet the needs of image hashing and information retrieval. In order to reduce the impact of data enhancement on hash codes, Jang et al [23] proposed a DHD scheme to minimize the diversity while exploring the potential of authenticated data. The correlation filtering hashing [35], asymmetric discrete hashing [36] and discrete class specific prototype hashing [37] proposed by Ma et al have achieved promising performance in narrowing semantic gaps, learning discriminative hash codes and making full use of semantic information.…”

Section: A Hashing Methods For Image Retrievalmentioning

confidence: 99%

“…The traditional hashing methods with deep features include MLH-CNN [9], KSH-CNN [10], and BRE-CNN [11]. The deep hashing methods include CNNH [13], DNNH [14], DRSCH [15], DSRH [16], DSH [17], DDH [18], RODH [38], MLSH [19], DBDH [20], CSQ [21], QSMIH [22] and DHD [23]. It should be noted that RODH uses a six-layer backbone network structure, and other hashing methods use the same backbone network as the RDHN0 in the proposed model.…”

Section: Comparisons Of Hashing Methods On Benchmark Datasetsmentioning

confidence: 99%

“…Therefore, we speculate that when β increases, the output of the hash code decreases, and a properly small hash code is helpful to the hash learning. However, when the hash code is too small, the sensitivity of the hash code will increase [23]. For example, the change of the real value of the hash code from 0.1 to -0.1 will be easy, and the value of the hash code will be changed after quantization (from 1 to -1), which is not conducive to the hash learning.…”

Section: ) Evaluation Of the Hyperparameter mentioning

confidence: 99%

“…With the development of deep learning, various deep learning-based hashing methods can not only learn more complex feature information, including semantic information, but also show excellent performance on largescale image retrieval. Currently, the representative deep hashing methods include Convolutional Neural Network Hashing (CNNH) [13], Deep Neural Networks Hashing (DNNH) [14], Deep Regularized Similarity Comparison Hashing (DRSCH) [15], Deep Semantic preserving and Ranking-based Hashing (DSRH) [16], Deep Supervised Hashing (DSH) [17], Discriminative Deep Hashing (DDH) [18], Multi-Level Supervised Hashing (MLSH) [19], Deep Balanced Discrete Hashing (DBDH) [20], Central Similarity Quantization (CSQ) [21], Quadratic Spherical Mutual Information Hashing (QSMIH) [22] and Deep Hash Distillation (DHD) [23].…”

Section: Introductionmentioning

confidence: 99%

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Ranking-Based Deep Hashing Network for Image Retrieval

Zhang

Xie

et al. 2022

IEEE Access

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In large-scale image retrieval, the deep learning-based hashing methods have significantly progressed. However, most of the existing deep hashing methods still have the problems of low feature learning efficiency and weak ranking relationship discrimination. To remedy these problems, a novel Ranking-based Deep Hashing Network (RDHN) is proposed for image retrieval in this paper, which integrates the feature learning module and hash learning module into a unified deep hashing network framework to jointly learn a powerful hash function so that the raw images can be mapped to discrete hash codes with significant discrimination. Specifically, a novel difference convolution is designed based on edge detection operators, and then it is uniquely applied to the first convolutional layer of the convolutional neural network (CNN), which can take advantage of the sensitive characteristics of edge detection operators for edge information to extract richer image edge information. Meanwhile, in hash learning, the ranking metric Mean Average Precision (MAP) is optimized using the idea of scaling, and then a ranking loss function based on MAP is carefully designed to enhance the neighborhood ranking capabilities of the hash codes. Furthermore, to reduce the quantification error, a quantization loss function is also designed. Finally, the ranking loss function is combined with the quantization loss function to form the objective function. The proposed method can generate high-quality discrete hash codes while learning to preserve ranking information, effectively improving retrieval performance. Extensive experimental results on three widely used benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art hashing approaches.

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Section: A Hashing Methods For Image Retrievalmentioning

confidence: 99%

Section: Comparisons Of Hashing Methods On Benchmark Datasetsmentioning

confidence: 99%

Section: ) Evaluation Of the Hyperparameter mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ranking-Based Deep Hashing Network for Image Retrieval

Zhang

Xie

et al. 2022

IEEE Access

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“…DCH-ReID means that different surveillance terminals capture image data and process it simultaneously to obtain feature codes for fast retrieval and then transmit these features to a remote ReID server for further processing, 12 which should be compressed or encoded to reduce bandwidth consumption. To balance the accuracy and efficiency of DCH-ReID retrieval, we drew on existing good work, such as References [13][14][15][16]. We propose a cross-modal model based on deep hashing to improve the retrieval efficiency of the algorithm using low bit-weight hash codes.…”

mentioning

confidence: 99%

Cross‐modal person re‐identification based on deep attention hash learning

Zhang,

Cao,

Zhang

et al. 2023

Concurrency and Computation

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SummaryPerson re‐identification based on text description is a critical task in modern security systems. However, existing methods primarily focus on performance and overlook the crucial aspect of retrieval efficiency. In this article, we propose a novel two‐stage multimodal re‐discovery algorithm called DCH‐ReID, which leverages attention hashing. First, we introduce a chunked mapping hash learning method that effectively mitigates confusion between hash codes. Second, we propose a hash learning approach based on the channel attention mechanism, assigning higher binary bit weights to important body parts. Finally, to balance retrieval performance and time efficiency, we present a two‐stage retrieval scheme. Through extensive experiments on the CUHK‐PEDES benchmark dataset, we validate that our proposed DCH‐ReID algorithm exhibits superior efficiency and higher accuracy compared to current mainstream text‐based pedestrian re‐identification algorithms.

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