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A Decade Survey of Content Based Image Retrieval using Deep Learning
[article]
2020
arXiv
pre-print
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This paper presents a comprehensive survey of deep learning based developments in the past decade for content based image retrieval. ...
Generally, the similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. ...
The pseudo triplets based unsupervised deep triplet hashing (UDTH) technique [112] is introduced for scalable image retrieval. ...
arXiv:2012.00641v1
fatcat:2zcho2szpzcc3cs6uou3jpcley
A Survey on Deep Hashing Methods
2022
ACM Transactions on Knowledge Discovery from Data
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Hashing is one of the most widely used methods for its computational and storage efficiency. With the development of deep learning, deep hashing methods show more advantages than traditional methods. ...
In this survey, we detailedly investigate current deep hashing algorithms including deep supervised hashing and deep unsupervised hashing. ...
We also thank Zeyu Ma, Huasong Zhong and Xiaokang Chen who discussed with us and provided instructive suggestions. ...
doi:10.1145/3532624
fatcat:7lxtu2qzvvhrpnjngefli2mvca
A Survey on Deep Hashing Methods
[article]
2022
arXiv
pre-print
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Hashing is one of the most widely used methods for its computational and storage efficiency. With the development of deep learning, deep hashing methods show more advantages than traditional methods. ...
In this survey, we detailedly investigate current deep hashing algorithms including deep supervised hashing and deep unsupervised hashing. ...
We also thank Zeyu Ma, Huasong Zhong and Xiaokang Chen who discussed with us and provided instructive suggestions. ...
arXiv:2003.03369v5
fatcat:m2iu3htilvgztkcazw3cyk6iqe
SSDH: Semi-supervised Deep Hashing for Large Scale Image Retrieval
2017
IEEE transactions on circuits and systems for video technology (Print)
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2017 pre-print arXiv:1607.08477v2 fatcat:otvsqvmtircbloy3epq5tbebpuOther Versions
Hashing methods have been widely used for efficient similarity retrieval on large scale image database. ...
However, these deep hashing methods are mainly designed for supervised scenarios, which only exploit the semantic similarity information, but ignore the underlying data structures. ...
Bit-scalable Deep Hashing The Bit-scalable Deep Hashing (BS-DRSCH) method [16] is also a one-stage deep hashing method that constructs an end-to-end architecture for hash function learning. ...
doi:10.1109/tcsvt.2017.2771332
fatcat:jbijzttotfap7ik5ngxv3xusyq
Pseudo Label based Unsupervised Deep Discriminative Hashing for Image Retrieval
2017
Proceedings of the 2017 ACM on Multimedia Conference - MM '17
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Hashing methods play an important role in large scale image retrieval. ...
In this paper, we propose a pseudo label based unsupervised deep discriminative hashing algorithm. First, we cluster images via K-means and the cluster labels are treated as pseudo labels. ...
Besides, we also compare our method with two unsupervised deep hashing method i.e. DH [1] and DeepBit [14] . For deep learning based methods, raw image pixels are directly used as input. ...
doi:10.1145/3123266.3123403
dblp:conf/mm/HuWCWL17
fatcat:ghdpic2fq5ewdpcey2h6s5dc34
Linear Distance Preserving Pseudo-Supervised and Unsupervised Hashing
2016
Proceedings of the 2016 ACM on Multimedia Conference - MM '16
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The experiments demonstrate that our pseudo-supervised method achieves consistent improvement for the state-of-the-art unsupervised hashing methods, while our unsupervised method outperforms the state-of-the-art ...
With the advantage in compact representation and efficient comparison, binary hashing has been extensively investigated for approximate nearest neighbor search. ...
[13] proposes a deep architecture for supervised hashing, in which images are mapped into binary codes via carefully designed deep neural networks. ...
doi:10.1145/2964284.2964334
dblp:conf/mm/WangZTZL16
fatcat:xwftms6jybg6jgvkkzuz4wxbia
Bridging Gap between Image Pixels and Semantics via Supervision: A Survey
[article]
2022
arXiv
pre-print
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Experiences are drawn from two application domains to illustrate this point: 1) object detection and 2) metric learning for content-based image retrieval (CBIR). ...
The fact that there exists a gap between low-level features and semantic meanings of images, called the semantic gap, is known for decades. Resolution of the semantic gap is a long standing problem. ...
Gattupalli et al. [50] developed a weakly supervised deep hashing method that used tag embeddings for image retrieval with the word2vec semantic embeddings. ...
arXiv:2107.13757v3
fatcat:dw4c74c3h5bvlmzmxugeh5aela
Learning to Hash for Indexing Big Data - A Survey
[article]
2015
arXiv
pre-print
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Prior randomized hashing methods, e.g., Locality-Sensitive Hashing (LSH), explore data-independent hash functions with random projections or permutations. ...
In addition, we also summarize recent hashing approaches utilizing the deep learning models. Finally, we discuss the future direction and trends of research in this area. ...
large-scale image retrieval with the learned binary image representation. ...
arXiv:1509.05472v1
fatcat:haj52w3cbbgszlmalfyu2kvzde
Deep Learning for Person Re-identification: A Survey and Outlook
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2001.04193v1 fatcat:ygecnhzmsnaangvs2v7rva2niu
We first conduct a comprehensive overview with in-depth analysis for closed-world person Re-ID from three different perspectives, including deep feature representation learning, deep metric learning and ...
With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. ...
For fast retrieval, hashing has been extensively studied to boost the searching speed, approximating the nearest neighbor search. ...
arXiv:2001.04193v2
fatcat:4d3thmsr3va2tnu72nawlu2wxy
Deep Discrete Hashing with Self-supervised Pairwise Labels
[article]
2017
arXiv
pre-print
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In this paper, we propose a novel unsupervised deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image retrieval and classification. ...
2) how to equip the binary representation with the ability of accurate image retrieval and classification in an unsupervised way? ...
Most of these deep hashing methods are supervised whose supervision information is given as triplet or pairwise labels. An example is the deep supervised hashing method by Li et al. ...
arXiv:1707.02112v1
fatcat:vn6km3w2fnhnbhccrlqu626gvu
Survey on Reliable Deep Learning-Based Person Re-Identification Models: Are We There Yet?
[article]
2020
arXiv
pre-print
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models being used for this task. ...
We present descriptions of each model along with their evaluation on a set of benchmark datasets. ...
and Interpretation of Events" with grant number 18/05668-3. ...
arXiv:2005.00355v1
fatcat:5msfk3apirg6vnpja52iw2qd3e
Cross-Model Hashing Retrieval Based on Deep Residual Network
2021
Computer systems science and engineering
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In the era of big data rich in We Media, the single mode retrieval system has been unable to meet people's demand for information retrieval. ...
This paper proposes a new solution to the problem of feature extraction and unified mapping of different modes: A Cross-Modal Hashing retrieval algorithm based on Deep Residual Network (CMHR-DRN). ...
Acknowledgement: This paper would like to thank all the authors cited in the reference for their contributions to this field. ...
doi:10.32604/csse.2021.014563
fatcat:maw3votu7bdl5csccvqniby65i
A deep locality-sensitive hashing approach for achieving optimal image retrieval satisfaction
2022
International Journal of Power Electronics and Drive Systems (IJPEDS)
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Besides, combining hashing methods with a deep learning architecture improves the image retrieval time and accuracy. ...
Hash tables are constructed from the extracted features and trained to achieve fast image retrieval. ...
Finally, a triplet ranking loss was designed for optimization. Lin et al. 2017 [29] presented a new discriminative deep hashing (DDH) network for image retrieval. ...
doi:10.11591/ijece.v12i3.pp2526-2538
fatcat:erj3ok7vrrfxhgbmevekvpiiuq
A Survey on Learning to Hash
[article]
2017
arXiv
pre-print
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Learning to hash is one of the major solutions to this problem and has been widely studied recently. ...
In this paper, we present a comprehensive survey of the learning to hash algorithms, categorize them according to the manners of preserving the similarities into: pairwise similarity preserving, multiwise ...
Speed up the Learning and Query Processes Scalable Hash Function Learning. ...
arXiv:1606.00185v2
fatcat:j5mnu7lfmvby5pfkg5pffk2nae
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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., +, TIP 2020 4643-4655 Scalable Deep Hashing for Large-Scale Social Image Retrieval. Cui, H., +, TIP 2020 1271-1284 Similarity-Preserving Linkage Hashing for Online Image Retrieval. ...
., +, TIP 2020 3626-3637
Scalable Deep Hashing for Large-Scale Social Image Retrieval. Cui, H., +,
TIP 2020 1271-1284
Similarity-Preserving Linkage Hashing for Online Image Retrieval. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
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