Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
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Distinctive Image Features from Scale-Invariant Keypoints
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Object retrieval with large vocabularies and fast spatial matching
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Lost in quantization: Improving particular object retrieval in large scale image databases
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Robust Wide Baseline Stereo from Maximally Stable Extremal Regions
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City-Scale Location Recognition
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Nearest-Neighbor Methods in Learning and Vision
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A contextual dissimilarity measure for accurate and efficient image search
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High-Dimensional Feature Matching: Employing the Concept of Meaningful Nearest Neighbors
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A Binning Scheme for Fast Hard Drive Based Image Search
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Scalable Person Re-identification: A Benchmark
Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.133 · Cited by 4,728 · see citing works
“In the last decade, a myriad of methods [15, 42, 45] have been developed to improve search performance.”
MethodCNN Features Off-the-Shelf: An Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451 · see citing works
Product Quantization for Nearest Neighbor Search
Jégou, H., Douze, M., & Schmid, C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.57 · Cited by 2,956 · see citing works
“Comparison with Hamming embedding methods: We compare our approach to spectral hashing (SH) [19], which maps vectors to binary signatures.”
MethodInfluentialSelf-citationAggregating local descriptors into a compact image representation
Jeǵou, H., Douze, M., Schmid, C., & Pérez, P. (2010) · 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.2010.5540039 · Cited by 2,679 · see citing works
“VLAD k=16 VLAD k=64 VLAD k=256 miniBOF [8] miniBOF 1 miniBOF 4 miniBOF 8 miniBOF 16 miniBOF 32”
BackgroundSelf-citationNetVLAD: CNN Architecture for Weakly Supervised Place Recognition
Arandjelović, R., Gronát, P., Torii, A., Pajdla, T., & Šivic, J. (2016) · 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.2016.572 · Cited by 3,441 · see citing works
“roves over current state-of-the-art compact image representations on standard image retrieval benchmarks. 1.1. Related work While there have been many improvements in designing better image retrieval [2,3,11,12,17,25,26,27,29, 32,48,51,52,53,54,71,78,79,82] and place recognition [4,9,10,15,16,24,35,44,46,63,64,65,75,8…”
BackgroundInfluentialGong, Y., Lazebnik, S., Gordo, A., & Perronnin, F. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.193 · Cited by 1,834 · see citing works
“The idea of rotating the data to minimize quantization loss can be found in [17] and [19].”
NetVLAD: CNN architecture for weakly supervised place recognitionPreprint
Arandjelović, R., Gronát, P., Torii, A., Pajdla, T., & Šivic, J. (2015) · arXiv (Cornell University) · Cited by 1,790 · see citing works
Aggregating Local Image Descriptors into Compact Codes
Jegou, H., Perronnin, F., Douze, M., Sanchez, J., Perez, P., & Schmid, C. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.235 · Cited by 1,503 · see citing works
“The INRIA Holidays dataset [26] is a collection of 1491 holiday images, 500 of them being used as queries.”
MethodSelf-citationSketch-based manga retrieval using manga109 dataset
Matsui, Y., Ito, K., Aramaki, Y., Fujimoto, A., Ogawa, T., Yamasaki, T., & Aizawa, K. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-4020-z · Cited by 1,454 · see citing works
Three things everyone should know to improve object retrieval
Arandjelović, R., & Zisserman, A. (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.2012.6248018 · Cited by 1,391 · see citing works
“[8, 10] have moved towards keeping a more accurate approximation of the original descriptor by Hamming embedding or product quantization.”
MethodExploring the Limits of Weakly Supervised Pretraining
Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., & van der Maaten, L. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01216-8_12 · Cited by 1,224 · see citing works
Neural Codes for Image Retrieval
Babenko, A., Slesarev, A., Chigorin, A., & Lempitsky, V. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10590-1_38 · Cited by 1,225 · see citing works
“We measure such performance on four standard benchmark datasets: INRIA Holidays [8], Oxford Buildings, Oxford Building 105K [19], and the University of Kentucky benchmark (UKB) [16].”
MethodInfluentialSiamese Instance Search for Tracking
Tao, R., Gavves, E., & Smeulders, A. W. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.158 · Cited by 1,208 · see citing works
“The most popular paradigm is based on matching local image descriptors between the query and the candidate image [40, 36, 20, 37, 45, 43] and is especially accurate for buildings [36].”
BackgroundMulti-scale Orderless Pooling of Deep Convolutional Activation Features
Gong, Y., Wang, L., Guo, R., & Lazebnik, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10584-0_26 · Cited by 1,043 · see citing works
“Section 4 presents experiments results for classification on three image datasets (SUN 397, MIT Indoor, and ILSVRC2012) and retrieval on the Holidays dataset.”
BackgroundInfluentialPerson Re-identification: Past, Present and FuturePreprint
Zheng, L., Yang, Y., & Hauptmann, A. G. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.02984 · Cited by 977 · see citing works
“The inverted index is a de facto data structure in the Bag-of-Words (BoW) based retrieval methods [22], [147], [148].”
BackgroundMethodWang, 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
“The reference set is from the Holiday images combined with Flickr1M [49].”
MethodCompressing Deep Convolutional Networks using Vector QuantizationPreprint
Gong, Y., Liu, L., Yang, M., & Bourdev, L. D. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.6115 · Cited by 1,047 · see citing works
“We performed experiments on the Holidays dataset (Jegou et al., 2008).”
MethodFastText.zip: Compressing text classification models
Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., & Mikolov, T. (2016) · arXiv · doi:10.48550/arxiv.1612.03651 · Cited by 887 · see citing works
Self-citationLarge-Scale Image Retrieval with Attentive Deep Local Features
Noh, H., Araújo, A. D. M., Sim, J., Weyand, T., & Han, B. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.374 · Cited by 851 · see citing works
“, object detection, visual place recognition, and product recognition. The last decades have witnessed tremendous advances in image retrieval systems—from handcrafted features and indexing algorithms [22,33,27,16] to, more recently, methods based on convolutional neural networks (CNNs) for global descriptor learning […”
BackgroundMethodInfluentialLarge-scale image retrieval with compressed Fisher vectors
Perronnin, F., Liu, Y., Sánchez, J., & Poirier, H. (2010) · 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.2010.5540009 · Cited by 770 · see citing works
Deep Image Retrieval: Learning Global Representations for Image Search
Gordo, A., Almazán, J., Revaud, J., & Larlus, D. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_15 · Cited by 787 · see citing works
“Finally, the INRIA Holidays dataset [55] is composed of 1,491 images and 500 different scene queries.”
MethodSIFT Meets CNN: A Decade Survey of Instance Retrieval
Zheng, L., Yang, Y., & Tian, Q. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2709749 · Cited by 758 · see citing works
“Many later works can be viewed as extensions of WGC.”
BackgroundMethodInfluentialImproving Bag-of-Features for Large Scale Image Search
Jeǵou, H., Douze, M., & Schmid, C. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-009-0285-2 · Cited by 736 · see citing works
“The Holidays dataset has been introduced in the preliminary version of this paper (Jégou et al. 2008) and results with HE+WCG have been improved here by adding a weighting, MA and query expansion.”
InfluentialSelf-citationArandjelović, R., & Zisserman, A. (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.2013.207 · Cited by 762 · see citing works
“Since the original introduction of the bag of visual words (BoW) formulation [16], there have been many notable contributions that have enhanced the descriptors [1, 15, 19], reduced quantization loss [6, 14, 18], and improved recall [1, 3, 4].”
BackgroundMethodInfluentialAggregating Local Deep Features for Image Retrieval
Yandex, A. B., & Lempitsky, V. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.150 · Cited by 679 · see citing works
“INRIA Holidays dataset [8] (Holidays) contains 1491 vacation snapshots corresponding to 500 groups each having the same scene or object.”
BackgroundMinimal Loss Hashing for Compact Binary Codes
Norouzi, M., & Blei, D. M. (2011) · Cited by 695 · see citing works
Appearance-only SLAM at large scale with FAB-MAP 2.0
Cummins, M., & Newman, P. M. (2010) · The International Journal of Robotics Research · doi:10.1177/0278364910385483 · Cited by 604 · see citing works
“Also of direct relevance is the research on content-based image retrieval systems in the computer vision community, where systems have been described that deal with more than a million images (Philbin et al. 2007; Chum et al. 2007; Nistér and Stewenius 2006; Jégou et al. 2008).”
BackgroundMethodResultLDAHash: Improved Matching with Smaller Descriptors
Strecha, C., Bronstein, A. M., Bronstein, M. M., & Fua, P. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.103 · Cited by 605 · see citing works
CNN Features off-the-shelf: an Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · arXiv · doi:10.48550/arxiv.1403.6382 · Cited by 607 · see citing works
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples
Radenović, F., Tolias, G., & Chum, O. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46448-0_1 · Cited by 554 · see citing works
“d on performance, measured via mean Average Precision (mAP), on validation tuples. 5.2 Test datasets and evaluation protocol We evaluate our approach on Oxford buildings [3], Paris [53] and Holidays1 [54] datasets. First two are closer to our training data, while the last dierentiates by containing similar scenes and…”
MethodInfluentialEnd-to-End Learning of Deep Visual Representations for Image Retrieval
Gordo, A., Almazán, J., Revaud, J., & Larlus, D. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1016-8 · Cited by 537 · see citing works
“Encoding techniques, such as the Fisher Vector (Perronnin and Dance, 2007; Perronnin et al, 2010) or the VLAD descriptor (Jégou et al, 2010) have been used for example by Perronnin et al (2010); Gordo et al (2012); Jégou and Chum (2012); Radenovic et al (2015).”
MethodSitzmann, V., Diamond, S., Peng, Y., Dun, X., Boyd, S., Heidrich, W., Heide, F., & Wetzstein, G. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201333 · Cited by 483 · see citing works
“We then sample images from a dataset of 1,244 highresolution images [Jegou et al. 2008] and assign one of the five depth settings randomly while considering all three wavelengths simultaneously.”
MethodData fusion through cross-modality metric learning using similarity-sensitive hashing
Bronstein, M. M., Bronstein, A. M., Michel, F., & Nikos Paragios, A. (2010) · 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.2010.5539928 · Cited by 461 · see citing works
“These ideas were successfully used in [32, 15] for content-based image retrieval as well as other computer vision applications [25].”
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
“They are often combined with compressed-domain representation and are especially appropriate for very large-scale settings [16], [22].”
Self-citationInLoc: Indoor Visual Localization with Dense Matching and View Synthesis
Taira, H., Okutomi, M., Sattler, T., Cimpoi, M., Pollefeys, M., Šivic, J., Pajdla, T., & Torii, A. (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.2018.00752 · Cited by 481 · see citing works
“Disloc represents images using bagof-visual-words with Hamming-Embedding [31] while also taking local descriptor space density into account.”
BackgroundMethodInfluentialSelective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval
Wei, X.-S., Luo, J.-H., Wu, J., & Zhou, Z. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2688133 · Cited by 462 · see citing works
Distance-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
“First, the INRIA Holidays data set [48], which consists of 1,491 images of 500 scenes and objects.”
MethodSketch-Based Image Retrieval: Benchmark and Bag-of-Features Descriptors
Eitz, M., Hildebrand, K., Boubekeur, T., & Alexa, M. (2010) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2010.266 · Cited by 411 · see citing works
“While the use of local descriptors is a common approach in example based image retrieval [4], [5], [6], [7], SBIR systems up to now still employ global descriptors and thus inherit their drawbacks, mainly being not invariant to affine transformations.”
MethodCross-Dimensional Weighting for Aggregated Deep Convolutional Features
Kalantidis, Y., Mellina, C., & Osindero, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46604-0_48 · Cited by 430 · see citing works
“For Holidays we use the “upright” version of the images.”
ResultInfluentialOn the burstiness of visual elements
Jeǵou, H., Douze, M., & Schmid, C. (2009) · 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.2009.5206609 · Cited by 404 · see citing works
Self-citationEfficient Object Category Recognition Using Classemes
Torresani, L., Szummer, M., & Fitzgibbon, A. W. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15549-9_56 · Cited by 415 · see citing works
“Although a number of systems satisfy these desiderata for object instance or place recognition [18,9] or for whole scene recognition [26], we argue that no existing system has addressed these requirements in the context of object category recognition.”
BackgroundPlaNet - Photo Geolocation with Convolutional Neural Networks
Weyand, T., Kostrikov, I., & Philbin, J. F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46484-8_3 · Cited by 406 · see citing works
“To test this, we evaluate the PlaNet features on the INRIA Holi-days dataset [23], consisting of 1,491 personal holiday photos, including landmarks, cities and natural scenes.”
MethodRevisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
Radenović, F., İşcen, A., Tolias, G., Avrithis, Y., & Chum, O. (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.2018.00598 · Cited by 406 · see citing works
“Larger distactor sets are used in the literature [33, 34, 16, 44] but none of them are standardized to provide a testbed for direct large scale comparison nor are they manually cleaned [16].”
BackgroundMethodInfluentialFrom generic to specific deep representations for visual recognition
Azizpour, H., Razavian, A. S., Sullivan, J., Maki, A., & Carlsson, S. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301270 · Cited by 401 · see citing works
“PASCAL VOC Object [9] H3D human attributes [6] Cat&Dog breeds [25] VOC Human Action [9] Holiday scenes [16] MIT 67 Indoor Scenes [29] Object attributes [10] Bird subordinate [39] Stanford 40 Actions [42] Paris buildings [27] SUN 397 Scene [41] SUN scene attributes [26] 102 Flowers [23] Visual Phrases [30] Sculptures […”
BackgroundCity-scale landmark identification on mobile devices
Chen, D. M., Baatz, G., Köser, K., Tsai, S. S., Vedantham, R., Pylvänäinen, T., Roimela, K., Chen, X., Bach, J., Pollefeys, M., et al. (2011) · 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.2011.5995610 · Cited by 404 · see citing works
“Visual landmark identification is closely related to the image retrieval (e.g. [9] [16]), object recognition (e.g. [21] [15]) or location recognition (e.g. [25] [24] [19] [26] [20] [14] [2] [11]) problems.”
BackgroundEvaluation of GIST descriptors for web-scale image search
Douze, M., Jeǵou, H., Sandhawalia, H., Amsaleg, L., & Schmid, C. (2009) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1646396.1646421 · Cited by 394 · see citing works
“It is derived from [5], which employs an inverted file [25].”
BackgroundMethodResultInfluentialSelf-citationFast ConvNets Using Group-Wise Brain Damage
Lebedev, V., & Lempitsky, V. (2016) · 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.2016.280 · Cited by 397 · see citing works
“2x speedup, we have found a negligible drop in performance for the INRIA holidays dataset [23] from 0.”
BackgroundBundling features for large scale partial-duplicate web image search
Wu, Z., Ke, Q., Isard, M., & Sun, J. (2009) · 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.2009.5206566 · Cited by 406 · see citing works
“State-of-the-art large scale image retrieval systems [1, 3, 11, 12] have relied on quantizing local SIFT descriptors [6] into visual words, and then applying scalable textual indexing and retrieval schemes [15].”
BackgroundMethodInfluentialHigh Capacity Reversible Data Hiding in Encrypted Images by Patch-Level Sparse Representation
Cao, X., Du, L., Wei, X., et al. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2423678 · Cited by 386 · see citing works
“After that, the particular comparisons between our method and the most state-of-the-art competitor [29] on Kodak,1 BOSSBase [39], PASCAL [40], and Holidays [41] are described.”
BackgroundMethodInfluentialVisual 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]…”
InfluentialUnifying Deep Local and Global Features for Image Search
Cao, B., Araújo, A. D. M., & Sim, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58565-5_43 · Cited by 393 · see citing works
“ry local descriptors against a large database of local descriptors, followed by geometrically verifying database images with sufficient number of correspondences. Bag-of-Words [54] and related methods [44,45,24] followed, by relying on visual words obtained via local descriptor clustering, coupled to TF-IDF scoring. Th…”
MethodAn analysis of the Viola-Jones face detection algorithm
Wang, Y.-Q. (2014) · Image Processing On Line · doi:10.5201/ipol.2014.104 · Cited by 354 · see citing works
Spatial Pyramid-Enhanced NetVLAD With Weighted Triplet Loss for Place Recognition
Yu, J., Zhu, C., Zhang, J., Huang, Q., & Tao, D. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2908982 · Cited by 355 · see citing works
“The typical global feature representations include bag-of-word (BoW) [12], [15], vector of locally aggregated descriptors (VLAD) [16], Hamming embedding [17], and Fisher vector (FV) [18].”
BackgroundExploiting local features from deep networks for image retrieval
Ng, J. Y.-H., Yang, F., & Davis, L. S. (2015) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2015.7301272 · Cited by 359 · see citing works
“On the Holidays dataset, the performance at scale 2 first increases and then decreases as we go up to higher layers.”
MethodInfluentialWolf, L., Hassner, T., & Taigman, Y. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.230 · Cited by 332 · see citing works
“We used the Hessian-Affine extractor and the SIFT [52] descriptor code made available by [67] to produce descriptors.”
Effective and Efficient Global Context Verification for Image Copy Detection
Zhou, Z., Wang, Y., Wu, Q. M. J., Yang, C., & Sun, X. (2016) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2016.2601065 · Cited by 336 · see citing works
“Recently, many geometric verification strategies [7], [12]–[17] have been proposed to reduce false matches.”
BackgroundMethodComparative Evaluation of Hand-Crafted and Learned Local Features
Schönberger, J. L., Hans, H., Sattler, T., & Pollefeys, M. (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.2017.736 · Cited by 313 · see citing works
“For the nearest neighbor search, we employ a state-of-the-art image retrieval system [38] using Hamming embedding [20] and visual burstiness weighting [21].”
MethodHello neighbor: Accurate object retrieval with k-reciprocal nearest neighbors
Qin, D., Gammeter, S., Bossard, L., Quack, T., & Gool, L. V. (2011) · 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.2011.5995373 · Cited by 313 · see citing works
“We evaluated our method on the Oxford5k [17, 2], Ox-ford105k [17], Paris [18, 3], University of Kentucky [16, 4] and the INRIA Holidays [10, 1] dataset.”
BackgroundMethodInfluentialFactors of Transferability for a Generic ConvNet Representation
Azizpour, H., Razavian, A. S., Sullivan, J., Maki, A., & Carlsson, S. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2500224 · Cited by 301 · see citing works
“mage ClassificationAttribute DetectionFine-grained RecognitionCompositionalInstance Retrieval PASCAL VOC Object [9] H3D human attributes [6] Cat&Dog breeds [29] VOC Human Action [9] Holiday scenes [17] MIT 67 Indoor Scenes [33] Object attributes [10] Bird subordinate [43] Stanford 40 Actions [46] Paris buildings [3…”
BackgroundNeural nano-optics for high-quality thin lens imaging
Tseng, E., Colburn, S., Whitehead, J., Huang, L., Baek, S., Majumdar, A., & Heide, F. (2021) · Nature Communications · doi:10.1038/s41467-021-26443-0 · Cited by 314 · see citing works
Image Retrieval for Image-Based Localization Revisited
Sattler, T., Weyand, T., Leibe, B., & Kobbelt, L. P. (2012) · British Machine Vision Conference (BMVC) · doi:10.5244/c.26.76 · Cited by 307 · see citing works
“propose to use Hamming embedding to cast votes only for a subset of all entries in a visual word [14].”
MethodInfluentialLocality sensitive hashing: A comparison of hash function types and querying mechanisms
Paulevé, L., Jeǵou, H., & Amsaleg, L. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.04.004 · Cited by 298 · see citing works
“…functions have recently been proposed to improve the performance of E2LSH, including the Leech lat-tices (Shakhnarovich et al., 2006) and E8 lattices (J´egou et al., 2008a), which o ff er strong quantization properties, and spherical hash functions (Terasawa and Tanaka, 2007) for unit-norm vectors.”
InfluentialSelf-citationQuery-adaptive late fusion for image search and person re-identification
Zheng, L., Wang, S., Tian, L., et al. (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.2015.7298783 · Cited by 299 · see citing works
“A 20k codebook is trained on Flickr60k dataset [10].”
BackgroundMethodInfluentialFast Matching of Binary Features
Muja, M., & Lowe, D. G. (2012) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2012.60 · Cited by 300 · see citing works
“Other works in which a Hamming embedding is computed from SIFT visual features are those of Jegou at al.”
MethodImage retrieval with geometry-preserving visual phrases
Zhang, Y., Jia, Z., & Chen, T. (2011) · 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.2011.5995528 · Cited by 292 · see citing works
Total recall II: Query expansion revisited
Chum, O., Mikulík, A., Perďoch, M., & Matas, J. (2011) · 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.2011.5995601 · Cited by 291 · see citing works
“In this paper, the approach of approximate k-means with 1-million visual words was chosen from the many different vocabulary construction methods, such as [18, 21, 9].”
BackgroundMethodGoogle Landmarks Dataset v2 – A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
Weyand, T., Araújo, A. D. M., Cao, B., & Sim, J. (2020) · 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/cvpr42600.2020.00265 · Cited by 309 · see citing works
“Oxford [42] 2007 11 55 5k Manual City Y Paris [43] 2008 11 55 6k Manual City Y Holidays [28] 2008 500 500 1.”
Girod, 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
Schönberger, J. L., Pollefeys, M., Geiger, A., & Sattler, 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.2018.00721 · Cited by 271 · see citing works
“This pipeline is based on a visual vocabulary tree embedded in a Hamming space [27] with visual burstiness weighting [28] and uses 2D-2D matching on the top-ranked retrievals to obtain 2D-3D correspondences for absolute pose estimation.”
Method[Paper] Visual Instance Retrieval with Deep Convolutional Networks
Razavian, A. S., Sullivan, J., Carlsson, S., & Maki, A. (2016) · ITE Transactions on Media Technology and Applications · doi:10.3169/mta.4.251 · Cited by 276 · see citing works
“, 2008), Sculptures6k (Arandjelović & Zisserman, 2011), Holidays (Jégou et al., 2008) and UKbench (Nistér & Stewénius, 2006).”
InfluentialFast 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
“Nearest neighbor (NN) search on binary codes has been used for image search [18, 23, 24], matching local features [9, 21], and parameter estimation [20].”
MethodEfficient representation of local geometry for large scale object retrieval
Perďoch, M., Chum, O., & Matas, J. (2009) · 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.2009.5206529 · Cited by 260 · see citing works
“The proposed method outperforms state of the art retrieval algorithms in a standard image retrieval benchmark.”
MethodTriangulation Embedding and Democratic Aggregation for Image Search
Jeǵou, H., & Zisserman, A. (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.2014.417 · Cited by 246 · see citing works
“Only when reducing to the vector to D′ = 128 components, our method gives on average slightly lower results than those reported by Arandjelović and Zisserman [2].”
BackgroundMethodResultInfluentialSelf-citationGet 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
“, more and more unrelated views are found among the top ranked images [22].”
BackgroundRobust Coverless Image Steganography Based on DCT and LDA Topic Classification
Zhang, X., Peng, F., & Long, M. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2838334 · Cited by 251 · see citing works
“1) The INRIA Holidays database [41]: this database is created by Herve Jegou et al.”
MethodSpatial coding for large scale partial-duplicate web image search
Zhou, W., Lu, Y., Li, H., Song, Y., & Tian, Q. C. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1874019 · Cited by 243 · see citing works
“Some instances of partial-duplicate web images are shown in Fig.”
BackgroundMethodInfluentialTurcot, P. J., & Lowe, D. G. (2009) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2009.5457541 · Cited by 233 · see citing works
“This weighting scheme can be interpreted as an approximation to a K-NN voting algorithm with tf-idf weights [8].”
MethodInfluentialA 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
Aggregating Deep Convolutional Features for Image Retrieval
Babenko, A., & Lempitsky, V. (2015) · arXiv · doi:10.48550/arxiv.1510.07493 · Cited by 224 · see citing works
“INRIA Holidays dataset [8] (Holidays) contains 1491 vacation snapshots corresponding to 500 groups each having the same scene or object.”
BackgroundGeometric min-Hashing: Finding a (thick) needle in a haystack
Chum, O., Perďoch, M., & Matas, J. (2009) · 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.2009.5206531 · Cited by 230 · see citing works
“Following recent work on image retrieval [24, 18, 21, 13], affine invariant features and descriptors [17] are used, images are represented as bags (or sets) of words (vector quantized descriptors) [24].”
MethodXCiT: Cross-Covariance Image TransformersPreprint
El-Nouby, A., Touvron, H., Caron, M., Bojanowski, P., Douze, M., Joulin, A., Laptev, I., Neverova, N., Synnaeve, G., Verbeek, J. J., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09681 · Cited by 273 · see citing works
Self-citationCloud-based robot grasping with the google object recognition engine
Kehoe, B., Matsukawa, A., Candido, S., Kuffner, J., & Goldberg, K. (2013) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2013.6631180 · Cited by 273 · see citing works
“For each object oi, we also capture a set of reference images at different viewpoints φi,j for j ∈ [1, Noi ].”
BackgroundKonolige, K., Bowman, J., Chen, J., Mihelich, P., Calonder, M., Lepetit, V., & Fua, P. (2010) · The International Journal of Robotics Research · doi:10.1177/0278364910370376 · Cited by 234 · see citing works
“For fast lookup of similar places, we rely on the hierarchica l vocabulary trees proposed by Nistér and Stewénius [26]; other methods include approximate nearest neighbor [30] an d various methods for improving the response or efficiency of the tree [8, 15, 16].”
MethodInfluentialMapillary Street-Level Sequences: A Dataset for Lifelong Place Recognition
Warburg, F., Hauberg, S., López-Antequera, M., Gargallo, P., Kuang, Y., & Civera, J. (2020) · 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/cvpr42600.2020.00270 · Cited by 235 · see citing works
“, Holidays [24], Oxford5k, Paris6k [40], Revisited Oxford5k and Paris6k [43], San Francisco Landmarks [10] and Google Landmarks [35, 38].”
BackgroundTo Aggregate or Not to aggregate: Selective Match Kernels for Image Search
Tolias, G., Avrithis, Y., & Jeǵou, H. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.177 · Cited by 207 · see citing works
“Hamming Embedding (HE) [10, 12] is a matching model that extends BOW by representing each local descriptor x with both its quantized value q ( x ) and a binary code b x of B bits.”
InfluentialSelf-citationData-driven visual similarity for cross-domain image matching
Shrivastava, A., Malisiewicz, T., Gupta, A., & Efros, A. A. (2011) · ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia) · doi:10.1145/2024156.2024188 · Cited by 217 · see citing works
“We used the bounded mean Average Precision (mAP) metric used by [Jégou et al. 2008] 1.”
BackgroundMethodInfluentialAvoiding Confusing Features in Place Recognition
Knopp, J., Šivic, J., & Pajdla, T. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15549-9_54 · Cited by 211 · see citing works
“This type of image matching has been shown to perform near real-time matching in datasets of 1M images [23, 18, 13].”
BackgroundMethodInfluentialObject retrieval and localization with spatially-constrained similarity measure and k-NN re-ranking
Shen, X., Lin, Z., Brandt, J., Avidan, S., & Wu, Y. N. (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.2012.6248031 · Cited by 206 · see citing works
Babenko, A., & Lempitsky, V. (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.2012.6248038 · Cited by 216 · see citing works
“We also note that while we combine multi-indices with the PQbased reranking [12] in some of our experiments, one can also employ binary embedding [25] or any other compression/fast distance computation scheme to rerank lists returned by a multi-index (or, depending on the application , omit the…”
MethodBabenko, A., & Lempitsky, V. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2361319 · Cited by 195 · see citing works
Fine-Tuning CNN Image Retrieval with No Human Annotation
Radenović, F., Tolias, G., & Chum, O. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2846566 · Cited by 1,515 · see citing works
“We evaluate our approach on Oxford buildings [33], Paris [68] and Holidays(2) [69] datasets.”
MethodA Survey on Deep Visual Place Recognition
Masone, C., & Barbara Caputo, B. (2021) · IEEE Access · doi:10.1109/access.2021.3054937 · Cited by 196 · see citing works
“They have also been combined with asymmetric distance computation [120] and multiple assignments [37], [118], [120], [122]–[124] to mitigate the quantization errors.”
BackgroundMethodInfluentialDeep Learning for Instance Retrieval: A Survey
Chen, W., Liu, Y., Wang, W., Bakker, E. M., Georgiou, T., Fieguth, P., Liu, L., & Lew, M. S. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3218591 · Cited by 282 · see citing works
“Holidays [51] consists of 1,491 images collected from personal holiday albums.”
BackgroundResultInfluentialCamera Pose Voting for Large-Scale Image-Based Localization
Zeisl, B., Sattler, T., & Pollefeys, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.310 · Cited by 179 · see citing works
“Similar to [2,12], who use orientations to improve image retrieval, we can thus use the local feature orientations to reject matches.”
MethodComposite Quantization for Approximate Nearest Neighbor Search
Zhang, T., Du, C., & Wang, J. (2014) · Cited by 188 · see citing works
Efficient Global 2D-3D Matching for Camera Localization in a Large-Scale 3D Map
Liu, L., Li, H., & Dai, Y. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.260 · Cited by 169 · see citing works
“For brevity we refer the reader to [24] for a precise definition of Hamming embedding distance.”
MethodQuery Specific Fusion for Image Retrieval
Zhang, S., Yang, M., Cour, T., Yu, K., & Metaxas, D. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33709-3_47 · Cited by 183 · see citing works
“HSV VOC Rank SVM Graph Graph [17] [31] [28] aggregation fusion PageRank density”
BackgroundMethodInfluentialPacking and Padding: Coupled Multi-index for Accurate Image Retrieval
Zheng, L., Wang, S., Liu, Z., & Tian, Q. (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.2014.250 · Cited by 203 · see citing works
“Specifically, methods such as Hamming Embedding [4] rebuild the discriminative ability of visual words by projecting SIFT descriptor into binary features.”
BackgroundMethodInfluentialAre Large-Scale 3D Models Really Necessary for Accurate Visual Localization?
Sattler, T., Torii, A., Šivic, J., Pollefeys, M., Taira, H., Okutomi, M., & Pajdla, T. (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.2017.654 · Cited by 169 · see citing works
“Disloc is a state-of-the-art method based on the BoW paradigm and Hamming embedding [20].”
MethodContextual weighting for vocabulary tree based image retrieval
Wang, X., Yang, M., Cour, T., Shenghuo Zhu, B., Yu, K., & Han, T. X. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126244 · Cited by 173 · see citing works
- Authors
- Jegou, H., Douze, M., & Schmid, C.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2008
- Volume
- 5302
- Pages
- 304-317
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1556531089
- Semantic Scholar
- 5a2bfaa724ba37134eb55c29644f8576c3d64c96
- Cited by
- 1,901
- References
- 16
- Citations by year
- 2026: 11 · 2025: 22 · 2024: 42 · 2023: 42 · 2022: 72 · 2021: 83 · 2020: 105 · 2019: 130 · 2018: 163 · 2017: 169 · 2016: 192 · 2015: 210 · 2014: 180 · 2013: 144 · 2012: 104
- FWCI
- 61.94
- Type
- conference paper
- Language
- en
- Open access
- green
- inria.hal.science
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- Agence Nationale de la Recherche (ANR-07-RIAM-0007, ANR-07-BLAN-0328)
- 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 › Information Systems › Information Retrieval and Search Behavior
- Keywords
- large-scale image search, bag of visual words, approximate nearest neighbor search, binary signatures, visual words, descriptor matching, inverted index, re-ranking, geometric transformation estimation, image retrieval
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