Product Quantization for Nearest Neighbor Search
Open accessAbstract
This paper introduces a product quantization-based approach for approximate nearest neighbor search. The idea is to decompose the space into a Cartesian product of low-dimensional subspaces and to quantize each subspace separately. A vector is represented by a short code composed of its subspace quantization indices. The euclidean distance between two vectors can be efficiently estimated from their codes. An asymmetric version increases precision, as it computes the approximate distance between a vector and a code. Experimental results show that our approach searches for nearest neighbors efficiently, in particular in combination with an inverted file system. Results for SIFT and GIST image descriptors show excellent search accuracy, outperforming three state-of-the-art approaches. The scalability of our approach is validated on a data set of two billion vectors.
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“Product quantization [14] is widely used in approximate nearest neighbor search, demonstrating better performance than hashing-based methods [21, 22].”
MethodImage retrieval using scene graphs
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DINOv2: Learning Robust Visual Features without Supervision
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“In particular, we heavily leverage its support for GPU-accelerated indices, using inverted file indices with product quantization codes (Jegou et al., 2010).”
MethodSelf-citationWang, J., Zhang, T., Song, J., Sebe, N., & Shen, H. T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699960 · Cited by 1,034 · see citing works
“BRE = binary reconstructive embedding [66], MLH = minimal loss hashing [107], LSH = locality sensitive hashing [14], ITQ = iterative quantization [35], [36], SH = spectral hashing [156], AGH-2 = two-layer hashing with graphs [86], USPLH = unsupervised sequential projection learning hashing [143], PQ = product quantiza…”
BackgroundMethodResultInfluentialDeep face recognition: A survey
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“[160] first used product quantization (PQ) [79] to directly retrieve the top-k most similar faces and re-ranked these faces by combining similarities from deep features and the COTS matcher [54].”
MethodSupervised Hashing for Image Retrieval via Image Representation Learning
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“…amount of available image data on the web, approximate nearest neighbor (ANN) search in large-scale datasets with millions or billions images has recently received considerable attention (Jegou, Douze, and Schmid 2011; Wang, Kumar, and Chang 2010; Kulis and Grauman 2009; Liu et al. 2012).”
BackgroundCompressing Deep Convolutional Networks using Vector QuantizationPreprint
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“In this work, instead of the traditional matrix factorization methods considered in (Denton et al., 2014; Jaderberg et al., 2014), we mainly consider a series of information theoretical vector quantization methods (Jegou et al., 2011; Chen et al., 2010) for compressing dense connected layers.”
MethodDeep Supervised Hashing for Fast Image Retrieval
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“Specifically, we choose product quantization [14] to compress the parameter matrix W ∈ Rm×n.”
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“Our image retrieval system is based on nearest neighbor search, which is implemented by a combination of KD-tree [7] and Product Quantization (PQ) [17].”
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“This includes work on patch encoding (see [40] for a recent comparison), on kernel-based methods [41] with a recent focus on explicit embeddings [42], [43], on dimensionality reduction [41] and on compression [44], [45], [46].”
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“Typical learning criteria include reconstruction error minimization [30, 12, 16] and graph learning[36, 24].”
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“Typical learning criteria include reconstruction error minimization (Salakhutdinov and Hinton 2007; Jegou, Douze, and Schmid 2011), neighborhood preserving as graph-based hashing (Weiss, Torralba, and Fergus 2009; Liu et al. 2011), and quantization error minimization as Iterative Quantization (ITQ)…”
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“how to represent an image? This includes works on patch encoding (see [4] for a recent comparison), on kernel-based methods [32] with a recent focus on explicit embeddings [20, 35], on dimensionality reduction [32] and on compression [13, 30, 36].”
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“The quantization is based on product quantization (Jegou et al., 2011; Baevski et al., 2020b) by choosing quantized representations from G = 2 codebooks with V = 320 entries each.”
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“This quantity is also known as Recall@R, the defacto metric [15] for image retrieving evaluation.”
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“file [33, 39] or product quantization [20] indexing techniques.”
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“The quantization module uses a Gumbel softmax to choose entries form the codebooks and the chosen entries are concatenated to obtain q (Jegou et al., 2011; Jang et al., 2016; Baevski et al., 2020a).”
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Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation
Li, X., Yu, L., Chen, H., Fu, C., Xing, L., & Heng, P. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2995319 · Cited by 550 · see citing works
“4) Implementation of π operation: Multi-scale ensembling is shown to be effective for image recognition [25–27].”
BackgroundMethodProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Gao, L., Zhu, X., Song, J., Zhao, Z., & Shen, H. T. (2019) · doi:10.24963/ijcai.2019 · Cited by 538 · see citing works
Learning to Hash for Indexing Big Data—A Survey
Wang, J., Liu, W., Kumar, S., & Chang, S. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2487976 · Cited by 524 · see citing works
“Unlike the recursive partitioning used by tree-based indexing methods, hashing methods repeatedly partition the entire dataset and derive a single hash ’bit’2 from each par- 2 Depending on the type of the hash function used, each hash may return either an integer or simply a binary bit.”
MethodHalder, R. K., Uddin, M. N., Uddin, M. A., Aryal, S., & Khraisat, A. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00973-y · Cited by 624 · see citing works
Hamming Distance Metric Learning
Norouzi, M., Fleet, D. J., & Salakhutdinov, R. (2012) · Cited by 562 · see citing works
Land Use Classification in Remote Sensing Images by Convolutional Neural NetworksPreprint
Castelluccio, M., Poggi, G., Sansone, C., & Verdoliva, L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.00092 · Cited by 560 · see citing works
“Strong improvements also come from using more recent encoding methods as done in [22], where Fisher vectors (FV) [23], vectors of locally aggregated descriptors (VLAD) [24], and vectors of locally aggregated tensors (VLAT) [25] are used in combination with HOG and color features.”
MethodPix3D: Dataset and Methods for Single-Image 3D Shape Modeling
Sun, X., Wu, J., Zhang, X., Zhang, Z., Zhang, C., Xue, T., Tenenbaum, J. B., & Freeman, W. T. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00314 · Cited by 456 · see citing works
“For each test image, we retrieve its K nearest neighbors from the test set, and use Recall@K [28] to compute how many retrieved images are actually depicting the same shape.”
MethodDouze, M., Guzhva, A., Deng, C., Johnson, J., Szilvasy, G., Mazaré, P.-E., Lomeli, M., Hosseini, L., & Jégou, H. (2026) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3618474 · Cited by 478 · see citing works
“Given this search algorithm, relying on a pure k-nearest neighbor graph is not optimal because neighbors are redundant.”
InfluentialSelf-citationWeakly Supervised Object Localization with Multi-Fold Multiple Instance Learning
Cinbis, R. G., Verbeek, J., & Schmid, C. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2535231 · Cited by 420 · see citing works
“We reduce the memory footprint, and speed up our iterative training procedure, by using the PQ and Blosc feature compression [3], [26].”
MethodSelf-citationLi, W., Zhang, Y., Sun, Y., Wang, W., Li, M., Zhang, W., & Lin, X. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2909204 · Cited by 501 · see citing works
“(PQ) [5] is a popular methods for ANNS, which decomposes”
A discriminative CNN video representation for event detection
Xu, Z., Yang, Y., & Hauptmann, A. G. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298789 · Cited by 438 · see citing works
“In addition, we propose to use Product Quantization [15] based on CNN video representation to speed up the execution (event search) time.”
MethodInfluentialDistance-Based Image Classification: Generalizing to New Classes at Near-Zero Cost
Mensink, T., Verbeek, J. J., Perronnin, F., & Csurka, G. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.83 · Cited by 426 · see citing works
“To allow metric learning on high-dimensional image features of datasets that are too large to fit in memory, we use in addition product quantization [14], a data compression technique that was recently used with success for large-scale image retrieval [15] and classifier training [1].”
Methodvq-wav2vec: Self-Supervised Learning of Discrete Speech RepresentationsPreprint
Baevski, A., Schneider, S., & Auli, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.05453 · Cited by 431 · see citing works
“In the following, we describe another strategy where we independently quantize partitions of z , similar to product quantization (Jegou et al., 2011).”
MethodK-Means Hashing: An Affinity-Preserving Quantization Method for Learning Binary Compact Codes
He, K., Wen, F., & Sun, J. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.378 · Cited by 395 · see citing works
“[10] further proposes the Asymmetric Distance Computation (ADC).”
BackgroundMethodInfluentialNegative Evidences and Co-occurences in Image Retrieval: The Benefit of PCA and Whitening
Jeǵou, H., & Chum, O. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33709-3_55 · Cited by 391 · see citing works
“In this paper, we will mainly focus on more scalable approaches in the spirit of recent work on compact images representations [6–8], where the image description is a short vector, which is subsequently encoded in compact codes using binarization [6, 9] or product quantization techniques [10].”
MethodResultOptimized Product Quantization
Ge, T., He, K., Ke, Q., & Sun, J. Z. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.240 · Cited by 398 · see citing works
“In the exhaustive search, the data are quantized into codewords [1], [8], [9]; the distances of vectors are approximated by the distances of codewords.”
BackgroundMethodInfluentialOptimized Product Quantization for Approximate Nearest Neighbor Search
Ge, T., He, K., Ke, Q., & Sun, J. Z. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.379 · Cited by 401 · see citing works
“Experiments show that our two solutions outperform the original PQ [10] and other alternatives like transform coding [3] and iterative quantization [6], even when the prior knowledge about the structure of the input data is used by PQ [10].”
BackgroundMethodInfluential(2021) · doi:10.1145/3448016.3457550 · Cited by 416 · see citing works
“• Vector query: This query type is the traditional vector similarity search [33, 41, 48, 49], where each entity is described”
BackgroundMethodInfluentialDeep Cauchy Hashing for Hamming Space Retrieval
Cao, Y., Long, M., Liu, B., & Wang, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00134 · Cited by 389 · see citing works
“Typical methods include reconstruction error minimization [27, 12, 14] and graph embedding [32, 23].”
MethodHashing for Similarity Search: A SurveyPreprint
Wang, J., Shen, H. T., Song, J., & Ji, J. (2014) · arXiv (Cornell University) · doi:10.4230/lipics.cpm.2023.20 · Cited by 458 · see citing works
A review of convolutional neural network architectures and their optimizations
Cong, S., & Zhou, Y. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10213-5 · Cited by 397 · see citing works
“developed a product quantization (PQ) algorithm for FC layers (Jegou et al. 2010).”
MethodHeo, J., Lee, Y., He, J., Chang, S., & Yoon, S. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248024 · Cited by 373 · see citing works
“• GIST-1M-960D: A set of 960 dimensional, one million GIST descriptors that are also used in [14].”
BackgroundSelf-supervised speech representation learning: A review
Mohamed, A., Lee, H.-Y., Borgholt, L., Havtorn, J. D., Edin, J., Igel, C., Kirchhoff, K., Li, S.-W., Livescu, K., Maaløe, L., et al. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3207050 · Cited by 372 · see citing works
“0 concatenates quantized representations from multiple codebooks at each timestep, so-called Product Quantization (PQ) [136].”
BackgroundVisual Place Recognition with Repetitive Structures
Torii, A., Šivic, J., Pajdla, T., & Okutomi, M. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.119 · Cited by 364 · see citing works
“Examples include: (i) learning better visual vocabularies [21, 28]; (ii) developing quantization methods less prone to quantization errors [14, 27, 44]; (iii) combining returns from multiple query images depicting the same scene [4, 6]; (iv) exploiting the 3D or graph structure of the database [11, 20, 29, 42, 43, 47]…”
Fast approximate nearest neighbor search with the navigating spreading-out graph
Fu, C., Xiang, C. J., Wang, C., & Cai, D. (2019) · Proceedings of the VLDB Endowment · doi:10.14778/3303753.3303754 · Cited by 384 · see citing works
Challenges in KNN Classification
Zhang, S. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3049250 · Cited by 367 · see citing works
“For example, Jegou, Douze and Schmid proposed a product quantization for nearest neighbor search [71].”
BackgroundLearning descriptors for object recognition and 3D pose estimation
Wohlhart, P., & Lepetit, V. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298930 · Cited by 452 · see citing works
“So far the only recognition approaches that have been demonstrated to work on large scale problems are based on Nearest Neighbor (NN) classification [23, 16, 8], because extremely efficient methods for NN search exist with an average complexity of O(1) [24, 21].”
MethodColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
Santhanam, K., Khattab, O., Saad-Falcon, J., Potts, C., & Zaharia, M. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.272 · Cited by 353 · see citing works
“Product quantization (Gray, 1984; Jegou et al., 2010) compresses a single vector by splitting it into small sub-vectors and encoding each of them using an ID within a codebook.”
BackgroundSmart Mining for Deep Metric Learning
Harwood, B., G, V. K. B., Carneiro, G., et al. (2017) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.307 · Cited by 351 · see citing works
“For the experiments, we follow the protocol used in previous papers [18, 19, 13], which uses unseen classes from the CUB- 200-2011 [23] and Cars196 [8] datasets in order to assess the clustering quality and k nearest neighbour retrieval [7].”
MethodDeep Learning Advances in Computer Vision with 3D Data
Ioannidou, A., Chatzilari, E., Nikolopoulos, S., & Kompatsiaris, I. Y. (2017) · ACM Computing Surveys · doi:10.1145/3042064 · Cited by 324 · see citing works
“It was proven experimentally that structured quantization methods like product quantization [Jegou et al. 2011] perform best while even a simple k-means can lead to satisfying results.”
Norouzi, M., & Fleet, D. J. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.388 · Cited by 306 · see citing works
“To derive the learning algorithm for ok-means we first rewrite the objective in matrix terms.”
BackgroundMethodInfluentialDeep Metric Learning via Facility Location
Song, H. O., Jegelka, S., Rathod, V., & Murphy, K. P. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.237 · Cited by 293 · see citing works
“Following the experimental protocol in [30, 29], we evaluate the clustering and k nearest neighbor retrieval [13] results on data from previously unseen classes on the CUB200-2011 [37], Cars196 [18], and Stanford Online Products [30] datasets.”
MethodSearching in one billion vectors: Re-rank with source coding
Jeǵou, H., Tavenard, R., Douze, M., & Amsaleg, L. (2011) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2011.5946540 · Cited by 317 · see citing works
“They are, however, significantly outperformed in terms of the trade-off between memory usage and accuracy by recent methods that cast high dimensional indexing to a source coding problem [11, 5, 6], in particular the product quantization-based method of [5] exhibits impressive results for large scale image search [6].”
BackgroundMethodInfluentialSelf-citationLocally Optimized Product Quantization for Approximate Nearest Neighbor Search
Kalantidis, Y., & Avrithis, Y. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.298 · Cited by 291 · see citing works
“All methods are non-exhaustive as in section 4.1, i.e. using a coarse quantizer, inverted lists and PQ-encoded residuals; however, all optimization variants are global except for LOPQ.”
BackgroundMethodInfluentialLinear cross-modal hashing for efficient multimedia search
Zhu, X., Huang, Z. H., Shen, H. T., & Zhao, X. (2013) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2502081.2502107 · Cited by 285 · see citing works
“…Search Process Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.”
BackgroundAdditive Quantization for Extreme Vector Compression
Babenko, A., & Lempitsky, V. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.124 · Cited by 289 · see citing works
“SIFT-1M: This dataset introduced in [10] contains one million of 128-dimensional SIFT descriptors [13], plus 100,000 descriptors in a hold out set.”
BackgroundMethodInfluentialGirod, B., Chandrasekhar, V., Chen, D. M., Cheung, N. M., Grzeszczuk, R., Reznik, Y. A., Gabriel Takacs, A., Tsai, S. S., & Vedantham, R. (2011) · IEEE Signal Processing Magazine · doi:10.1109/msp.2011.940881 · Cited by 290 · see citing works
“The VQ schemes based on the product quantizer [ 34 ] or a tree-structured vector quantizer [10] are complex and require storage of large codebooks on the mobile device.”
Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost
Mensink, T., Verbeek, J., Perronnin, F., & Csurka, G. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33709-3_35 · Cited by 281 · see citing works
“Product quantization (PQ) was introduced in [6] as a lossy compression mechanism for local SIFT descriptors in a bag-of-features image retrieval system.”
Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge
Hinami, R., Mei, T., & Satoh, S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.391 · Cited by 277 · see citing works
Delving deep into spatial pooling for squeeze-and-excitation networks
Jin, X. Y., Xie, Y., Wei, X.-S., Zhao, B., Chen, Z.-M., & Tan, X. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108159 · Cited by 294 · see citing works
Embedding-based Retrieval in Facebook Search
Huang, J.-T., Sharma, A., Sun, S., Xia, L., Zhang, D., Pronin, P., Padmanabhan, J., Ottaviano, G., & Yang, L. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403305 · Cited by 331 · see citing works
“onents for the embedding quantization, one is the coarse quantization which quantizes embedding vectors into coarse clusters typically through K-means algorithm, and the other is product quantization [8] which does a fine-grained quantization to enable efficient calculation of embedding distances. There are several im…”
High-dimensional signature compression for large-scale image classification
Sanchez, J., & Perronnin, F. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995504 · Cited by 272 · see citing works
“[13] proposed product quantizers (PQ) for NN-search of SIFT vectors.”
BackgroundMethodFast search in Hamming space with multi-index hashing
Norouzi, M., Punjani, A., & Fleet, D. J. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248043 · Cited by 263 · see citing works
“, [1, 10, 15]), we restrict our attention in this paper to the use of compact binary codes.”
BackgroundWang, M., Xu, X., Yue, Q., & Wang, Y. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3476249.3476255 · Cited by 273 · see citing works
Get Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization
Lynen, S., Sattler, T., Bosse, M., Hesch, J., Pollefeys, M., & Siegwart, R. (2015) · Robotics: Science and Systems XI · doi:10.15607/rss.2015.xi.037 · Cited by 243 · see citing works
WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia
Schwenk, H., Chaudhary, V., Sun, S., Gong, H., & Guzmán, F. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.115 · Cited by 317 · see citing works
“In this work, we chose a rather aggressive compression based on a 64-bit productquantizer (Jégou et al., 2011), and portioning the search space in 32k cells.”
MethodREMIND Your Neural Network to Prevent Catastrophic Forgetting
Hayes, T. L., Kafle, K., Shrestha, R., Acharya, M., & Kanan, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58598-3_28 · Cited by 269 · see citing works
Optimizing Dense Retrieval Model Training with Hard Negatives
Zhan, J., Mao, J., Liu, Y., Guo, J., Zhang, M., & Ma, S. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462880 · Cited by 248 · see citing works
Jin, Z., Li, C., Lin, Y., & Cai, D. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2283497 · Cited by 226 · see citing works
“ing methods [3], [6], [13]–[16], [18]–[20], [25], [28]–[32],”
BackgroundA neural algorithm for a fundamental computing problem
Dasgupta, S., Stevens, C. F., & Navlakha, S. (2017) · Science · doi:10.1126/science.aam9868 · Cited by 213 · see citing works
A survey on Visual-Based Localization: On the benefit of heterogeneous data
Piasco, N., Désiré Sidibé, A., Demonceaux, C., & Gouet-Brunet, V. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.09.013 · Cited by 238 · see citing works
Deep Quantization Network for Efficient Image Retrieval
Cao, Y., Long, M., Wang, J., Zhu, H., & Wen, Q. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10455 · Cited by 224 · see citing works
“…in M codebooks can be pre-computed and stored in a queryspecific M ×K lookup table, which is used to compute AQD between the query and all database points h i , each entails M table lookups and additions and is only slightly more costly than Hamming distance (Jegou, Douze, and Schmid 2011).”
BackgroundMethodInfluentialRecent advances in efficient computation of deep convolutional neural networks
Cheng, J., Wang, P., Li, G., Hu, Q., & Lu, H. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700789 · Cited by 230 · see citing works
“In addition, the product quantization algorithm (PQ) [36], a special case of vector quantization, was leveraged to compress the fully connected layers.”
MethodA Sparse Embedding and Least Variance Encoding Approach to Hashing
Zhu, X., Zhang, L., & Huang, Z. H. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2332764 · Cited by 226 · see citing works
Efficient Natural Language Response Suggestion for Smart ReplyPreprint
Henderson, M., Al‐Rfou, R., Strope, B. P., Sung, Y.-H., Lukacs, L., Guo, R., Kumar, S., Miklos, B., & Kurzweil, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.00652 · Cited by 315 · see citing works
Revisiting the VLAD image representation
Delhumeau, J., Gosselin, P.-H., Jeǵou, H., & Pérez, P. (2013) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2502081.2502171 · Cited by 214 · see citing works
“Table 3 shows that the relative improvement of our technique is comparatively reduced when applying dimensionality reduction and using compact codes obtained by product quantization [7].”
BackgroundMethodInfluentialA Survey on Deep Hashing Methods
Luo, X., Wang, H., Wu, D., Chen, C., Deng, M., Huang, J., & Hua, X.-S. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3532624 · Cited by 241 · see citing works
“Similar statement was proposed in [62] from the statistical perspective: the distance reconstruction error is statistically bounded by the quantization error.”
Background
- Authors
- Jégou, H., Douze, M., & Schmid, C.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2011-01
- Volume
- 33
- Issue
- 1
- Pages
- 117-128
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2124509324
- Semantic Scholar
- 4748d22348e72e6e06c2476486afddbc76e5eca7
- Cited by
- 2,956
- References
- 35
- Citations by year
- 2026: 260 · 2025: 295 · 2024: 233 · 2023: 211 · 2022: 195 · 2021: 204 · 2020: 187 · 2019: 207 · 2018: 201 · 2017: 184 · 2016: 196 · 2015: 196 · 2014: 159 · 2013: 116 · 2012: 71
- FWCI
- 84.24
- Type
- journal article
- Language
- en
- Open access
- green
- inria.hal.science
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion, other-oa)
- 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 › Advanced Data Compression Techniques
- Keywords
- product quantization, approximate nearest neighbor search, subspace decomposition, inverted index, SIFT descriptors, GIST descriptors, image retrieval, scalability
- MeSH terms
- Algorithms, Artificial Intelligence, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Pattern Recognition, Automated, Models, Statistical, Cluster Analysis, Information Storage and Retrieval
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