The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
Open accessOverview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The text we found for this paper looks like another document, so the self-check wasn’t run.
Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Robust wide-baseline stereo from maximally stable extremal regions
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2004) · Image and Vision Computing · doi:10.1016/j.imavis.2004.02.006 · Cited by 3,760
Scalable Recognition with a Vocabulary Tree
Nistér, D., & Stewénius, H. (2006) · 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.2006.264 · Cited by 3,641
Scale & Affine Invariant Interest Point Detectors
Mikolajczyk, K., & Schmid, C. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000027790.02288.f2 · Cited by 3,436
Self-citationLocality-sensitive hashing scheme based on p-stable distributions
Datar, M., Immorlica, N., Indyk, P., & Mirrokni, V. (2004) · Annual Symposium on Computational Geometry (SoCG) · doi:10.1145/997817.997857 · Cited by 2,971
Object retrieval with large vocabularies and fast spatial matching
Philbin, J. F., Chum, O., Isard, M., Šivic, J., & Zisserman, A. (2007) · 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.2007.383172 · Cited by 2,931
Lost in quantization: Improving particular object retrieval in large scale image databases
Philbin, J., Chum, O., Isard, M., Sivic, J., & Zisserman, A. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587635 · Cited by 1,478
Robust Wide Baseline Stereo from Maximally Stable Extremal Regions
Matas, J., Chum, O., Urban, M., & Pajdla, T. (2002) · Procedings of the British Machine Vision Conference 2002 · doi:10.5244/c.16.36 · Cited by 1,350
City-Scale Location Recognition
Schindler, G., Brown, M., & Szeliski, R. (2007) · 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.2007.383150 · Cited by 622
Cited by
Sorted by citations
Items 601–700 of 1,000
Towards efficient privacy-preserving encrypted image search in cloud computing
Wang, Y., Miao, M., Shen, J., & Wang, J. (2017) · Soft Computing · doi:10.1007/s00500-017-2927-6 · Cited by 25
Fusion that matters: convolutional fusion networks for visual recognition
Liu, Y., Guo, Y., Georgiou, T., et al. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5691-4 · Cited by 24
Exploiting tf-idf in deep Convolutional Neural Networks for Content Based Image Retrieval
Kondylidis, N., Tzelepi, M., & Tefas, A. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6212-1 · Cited by 25
Composite Description Based on Salient Contours and Color Information for CBIR Tasks
Iakovidou, C., Anagnostopoulos, N., Lux, M., Christodoulou, K., Boutalis, Y. S., & Chatzichristofis, S. A. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2894281 · Cited by 25
Liang, L., Zhao, W., Hao, X., Yang, Y., Yang, K., Liang, L., & Yang, Q. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.06.101 · Cited by 24
Joint Coding of Local and Global Deep Features in Videos for Visual Search
Ding, L., Tian, Y., Fan, H., Chen, C., & Huang, T. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2965306 · Cited by 21
Hamming embedding similarity-based image classification
Jain, M., Benmokhtar, R., Jeǵou, H., & Gros, P. (2012) · International Conference on Multimedia Retrieval · doi:10.1145/2324796.2324820 · Cited by 27
Self-citationEfficient tree construction for multiscale image representation and processing
Havel, J., Merciol, F., & Lefèvre, S. (2016) · Journal of Real-Time Image Processing · doi:10.1007/s11554-016-0604-0 · Cited by 23
DeepHash for Image Instance Retrieval
Lin, J., Morère, O., Veillard, A., Duan, L., Goh, H., & Chandrasekhar, V. (2017) · International Conference on Multimedia Retrieval · doi:10.1145/3078971.3078983 · Cited by 21
Multiple Distance-Based Coding: Toward Scalable Feature Matching for Large-Scale Web Image Search
Zhou, Z., Wu, Q. M. J., & Sun, X. (2019) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2919570 · Cited by 23
Metric learning-based kernel transformer with triplets and label constraints for feature fusion
Kan, S., Zhang, L., He, Z., Cen, Y., Chen, S., & Zhou, J. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.107086 · Cited by 23
Near-Duplicate Image Detection in a Visually Salient Riemannian Space
Zheng, L., Lei, Y., Qiu, G., & Huang, J. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2206386 · Cited by 22
PIC: Enable Large-Scale Privacy Preserving Content-Based Image Search on Cloud
Zhang, L., Jung, T., Feng, P., Liu, K., Li, X., & Liu, Y. (2015) · Proceedings of the International Conference on Parallel Processing · doi:10.1109/icpp.2015.104 · Cited by 23
An efficient fast-response content-based image retrieval framework for big data
Sakr, N. A., Eldesouky, A. I., & Arafat, H. (2016) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2016.04.015 · Cited by 23
Exploring spatial and channel contribution for object based image retrieval
Shi, X., & Qian, X. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.104955 · Cited by 22
Quantization schemes for low bitrate Compressed Histogram of Gradients descriptors
Chandrasekhar, V., Reznik, Y. A., Gabriel Takacs, A., Chen, D. M., Tsai, S. S., Grzeszczuk, R., & Girod, B. (2010) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2010.5543242 · Cited by 23
Factors of Transferability for a Generic ConvNet RepresentationPreprint
Azizpour, H., Razavian, A. S., Sullivan, J., Maki, A., & Carlsson, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.5774 · Cited by 20
A method for video authenticity based on the fingerprint of scene frame
Mao, J., Xiao, G., Sheng, W., et al. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.09.001 · Cited by 22
Metric imitation by manifold transfer for efficient vision applications
Dai, D., Kroeger, T., Timofte, R., & Gool, L. 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.2015.7298975 · Cited by 20
Statistical quantization for similarity search
Wang, Q., Zhu, G., & Yuan, Y. (2014) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2014.03.002 · Cited by 23
Rotation and flipping robust region binary patterns for video copy detection
Kim, S., Lee, S. H., & Ro, Y. M. (2013) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2013.12.003 · Cited by 22
Max-SIFT: Flipping invariant descriptors for Web logo search
Xie, L., Tian, Q., & Zhang, B. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7026156 · Cited by 22
Searching visual instances with topology checking and context modeling
Zhang, W., & Ngo, C. (2013) · International Conference on Multimedia Retrieval · doi:10.1145/2461466.2461477 · Cited by 22
Image retrieval using indexed histogram of Void-and-Cluster Block Truncation Coding
Guo, J.-M., Prasetyo, H., Lee, H., et al. (2015) · Signal Processing · doi:10.1016/j.sigpro.2015.11.009 · Cited by 22
Online learning to rank for content-based image retrieval
Wan, J., Wu, P., Hoi, S. C. H., Zhao, P., Gao, X., WANG, D., Zhang, Y., & Li, J. (2015) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 22
Fast-forwarding to desired visualizations with zenvisage
Siddiqui, T., Lee, J. J., Kim, A., et al. (2017) · Conference on Innovative Data Systems Research · Cited by 22
Co-occurrence of deep convolutional features for image search
Forcen, J. I., Pagola, M., Barrenechea, E., & Bustince, H. (2020) · Image and Vision Computing · doi:10.1016/j.imavis.2020.103909 · Cited by 21
Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval
Yokoo, S., Ozaki, K., Simo‐Serra, E., & Iizuka, S. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00514 · Cited by 23
Fu, H., Kong, X., & Lu, J. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.05.033 · Cited by 22
Fast image copy detection approach based on local fingerprint defined visual words
Ling, H., Yan, L., Zou, F., Liu, C., & Feng, H. (2012) · Signal Processing · doi:10.1016/j.sigpro.2012.08.011 · Cited by 21
Araújo, A. D. M., Chaves, J., Chen, D., et al. (2015) · ACM Multimedia Systems Conference (MMSys) · doi:10.1145/2713168.2713197 · Cited by 22
A hierarchal BoW for image retrieval by enhancing feature salience
Jiang, F., Hu, H., Zheng, J. Y., & Li, B. H. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.10.044 · Cited by 22
Neighborhood Matching for Image Retrieval
González-Díaz, I., Birinci, M., Díaz-de-María, F., & Delp, E. J. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2616298 · Cited by 22
Effective real-scenario Video Copy Detection
Zhang, Y., & Zhang, X. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7900252 · Cited by 22
SURF binarization and fast codebook construction for image retrieval
Kan, S., Cen, Y., Cen, Y., et al. (2017) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.08.006 · Cited by 22
Shankar, D. D., & Azhakath, A. S. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09820-7 · Cited by 22
GPR1200: A Benchmark for General-Purpose Content-Based Image Retrieval
Schall, K., Barthel, K. U., Hezel, N., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-98358-1_17 · Cited by 22
Geometric VLAD for Large Scale Image SearchPreprint
Wang, Z., Wei, D., Bhardwaj, A., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.3829 · Cited by 22
Architecture-Adaptive Code Variant Tuning
Muralidharan, S., Roy, A., Hall, M. W., Garland, M., & Rai, P. (2016) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/2872362.2872411 · Cited by 20
Binary Sketches for Secondary Filtering
Míč, V., Novák, D., & Zezula, P. (2018) · ACM Transactions on Information Systems · doi:10.1145/3231936 · Cited by 21
Traffic surveillance video coding with libraries of vehicles and background
Ma, C., Liu, D., Peng, X., et al. (2019) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2019.03.009 · Cited by 21
Garg, S., & Milford, M. J. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196827 · Cited by 22
Han, X., Chen, Y., & Ruan, X. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2168417 · Cited by 21
Towards large-scale geometry indexing by feature selection
Tolias, G., Kalantidis, Y., Avrithis, Y., & Kollias, S. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.12.002 · Cited by 21
Visual synonyms for landmark image retrieval
Gavves, E., Snoek, C. G. M., & Smeulders, A. W. M. (2011) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2011.10.004 · Cited by 20
Simple Techniques Make Sense: Feature Pooling and Normalization for Image Classification
Xie, L., Tian, Q., & Zhang, B. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2461978 · Cited by 21
Deep Permutations: Deep Convolutional Neural Networks and Permutation-Based Indexing
Amato, G. M., Falchi, F., Gennaro, C., & Vadicamo, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46759-7_7 · Cited by 22
Fast, Compact, and Discriminative: Evaluation of Binary Descriptors for Mobile Applications
Madeo, S., & Bober, M. Z. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2615521 · Cited by 21
Advanced Techniques in CBIR: Local Descriptors, Visual Dictionaries and Bags of Features
Junior, E. A. D. V., & Cord, M. (2009) · doi:10.1109/sibgrapi-tutorials.2009.14 · Cited by 21
A Hybrid Deep Learning Architecture for Latent Topic-based Image Retrieval
Arun, K. S., & Govindan, V. K. (2018) · Data Science and Engineering · doi:10.1007/s41019-018-0063-7 · Cited by 21
A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps
Rajasegaran, J., Karunanayake, N., Gunathillake, A., Seneviratne, S., & Jourjon, G. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313427 · Cited by 19
Simultaneous Feature Aggregating and Hashing for Compact Binary Code Learning
Do, T.-T., Le, K., Hoang, T., Le, H., Nguyen, T. V., & Cheung, N. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2913509 · Cited by 22
Unsupervised selective rank fusion for image retrieval tasks
Valem, L. P., & Pedronette, D. C. G. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.09.065 · Cited by 21
A signature-based bag of visual words method for image indexing and search
dos Santos, J. M., de Moura, E. S., da Silva, A. S., Cavalcanti, J. M. B., Torres, R. D. S., & Vidal, M. L. A. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.06.023 · Cited by 21
Coding video sequences of visual features
Baroffio, L., Cesana, M., Redondi, A. E. C., et al. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738390 · Cited by 20
Projection Bank: From High-Dimensional Data to Medium-Length Binary Codes
Liu, L., Yu, M., & Shao, L. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.323 · Cited by 24
Mehmood, Z., Rashid, M., Rehman, A. U., et al. (2018) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-171137 · Cited by 20
Improving cross-dimensional weighting pooling with multi-scale feature fusion for image retrieval
Wang, Q., Lai, J., Yang, Z., Xu, K. M., Kan, P., Liu, W., & Lei, L. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.08.025 · Cited by 20
Learning Token-Based Representation for Image Retrieval
Wu, H., Wang, M., Zhou, W., et al. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20173 · Cited by 1
In-Memory Search With Phase Change Device-Based Ternary Content Addressable Memory
Yang, L., Zhao, R., Li, Y., et al. (2022) · IEEE Electron Device Letters · doi:10.1109/led.2022.3179736 · Cited by 20
Amjoud, A. B., & AMROUCH, M. (2022) · IEEE Access · doi:10.1109/access.2022.3225455 · Cited by 17
Introducing Space and Time in Local Feature-Based Endomicroscopic Image Retrieval
André, B., Vercauteren, T. K., Aymeric Perchant, B., Buchner, A. M., Wallace, M. B., & Ayache, N. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-11769-5_2 · Cited by 20
Large scale image retrieval with visual groups
Dai, L., Sun, X., Wu, F., et al. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738532 · Cited by 20
On the Exploration of Convolutional Fusion Networks for Visual Recognition
Liu, Y., Guo, Y., & Lew, M. S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-51811-4_23 · Cited by 19
Extreme Value Theory-Based Methods for Visual Recognition
Scheirer, W. J. (2017) · Synthesis lectures on computer vision · doi:10.1007/978-3-031-01817-6 · Cited by 24
Laplacian Coordinates: Theory and Methods for Seeded Image Segmentation
Casaca, W. C. D. O., Gois, J. P., Batagelo, H. C., et al. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2974475 · Cited by 20
Zhou, Z., Lin, K., Cao, Y., Yang, C., & Liu, Y. (2020) · Mathematics · doi:10.3390/math8040644 · Cited by 22
Hare, J. S., Samangooei, S., Dupplaw, D., & Lewis, P. H. (2012) · International Conference on Multimedia Retrieval · doi:10.1145/2324796.2324844 · Cited by 18
A survey on image retrieval performance of different bag of visual words indexing techniques
Mukherjee, J., Mukhopadhyay, J., & Mitra, P. (2014) · IEEE Students’ Technology Symposium · doi:10.1109/techsym.2014.6807922 · Cited by 19
Efficient Large-Scale Similarity Search Using Matrix Factorization
İşcen, A., Rabbat, M., & Furon, T. (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.2016.228 · Cited by 20
Collaborative Image Relevance Learning for Visual Re-Ranking
Ouyang, J., Zhou, W., Wang, M., et al. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3029886 · Cited by 19
Directional local ternary co-occurrence pattern for natural image retrieval
Singhal, A., Agarwal, M., & Pachori, R. B. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10319-4 · Cited by 18
Image retrieval using contrastive weight aggregation histograms
Lu, F., & Liu, G. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103457 · Cited by 19
BIG-OH: BInarization of gradient orientation histograms
Baber, J., Dailey, M. N., Satoh, S., Afzulpurkar, N., & Bakhtyar, M. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.08.006 · Cited by 19
Scalable Image Search with Multiple Index Tables
Cai, J., Liu, Q., Chen, F., et al. (2014) · doi:10.1145/2578726.2578780 · Cited by 19
FIND: A Neat Flip Invariant Descriptor
Guo, X., & Cao, X. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.131 · Cited by 19
Balancing clusters to reduce response time variability in large scale image search
Tavenard, R., Jeǵou, H., & Amsaleg, L. (2011) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2011.5972514 · Cited by 18
Self-citationLin, Y., Lin, Z., & Zha, H. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2629452 · Cited by 19
Subspace-based multi-view fusion for instance-level image retrieval
Li, J., Yang, B., Yang, W., Sun, C., & Xu, J. (2020) · The Visual Computer · doi:10.1007/s00371-020-01828-2 · Cited by 19
Image retrieval for Structure-from-Motion via Graph Convolutional Network
Yan, S., Zhang, M., Lai, S., et al. (2021) · Information Sciences · doi:10.1016/j.ins.2021.05.050 · Cited by 19
Topological Spatial Verification for Instance Search
Zhang, W., & Ngo, C. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2440997 · Cited by 18
Local residual similarity for image re-ranking
Sun, S., Li, Y., Zhou, W., et al. (2017) · Information Sciences · doi:10.1016/j.ins.2017.07.004 · Cited by 18
Dataset-Driven Unsupervised Object Discovery for Region-Based Instance Image Retrieval
Zhang, Z., Wang, L., Wang, Y., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3141433 · Cited by 18
Landmark image retrieval using visual synonyms
Gavves, E., & Snoek, C. G. M. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1874166 · Cited by 18
Contextual synonym dictionary for visual object retrieval
Tang, W., Cai, R., Li, Z., et al. (2011) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2072298.2072364 · Cited by 18
Behavior Discovery and Alignment of Articulated Object Classes from Unstructured Video
Pero, L. D., Ricco, S., Sukthankar, R., & Ferrari, V. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0939-9 · Cited by 18
Two-stream Attentive CNNs for Image Retrieval
Yang, F., Li, J., Wei, S., et al. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123396 · Cited by 18
Region-Based Prediction for Image Compression in the Cloud
Bégaint, J., Thoreau, D., Guillotel, P., et al. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2788192 · Cited by 17
Chen, Z., Wang, S., Wu, D. O., Huang, T., & Duan, L. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240654 · Cited by 16
Unsupervised Deep Hashing With Adaptive Feature Learning for Image Retrieval
Zhu, Y., Li, Y., & Wang, S. (2019) · IEEE Signal Processing Letters · doi:10.1109/lsp.2019.2892233 · Cited by 18
Vision-based sparse topological mapping
Korrapati, H., & Mezouar, Y. (2014) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2014.03.015 · Cited by 18
Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval
Morère, O., Lin, J., Veillard, A., Duan, L., Chandrasekhar, V., & Poggio, T. (2017) · International Conference on Multimedia Retrieval · doi:10.1145/3078971.3078987 · Cited by 19
An Adaptive Weight Method for Image Retrieval Based Multi-Feature Fusion
Lu, X., Wang, J., Li, X., et al. (2018) · Entropy · doi:10.3390/e20080577 · Cited by 18
Jeǵou, H., Douze, M., & Schmid, C. (2009) · Cited by 19
Self-citationThe PS-Battles Dataset - an Image Collection for Image Manipulation Detection
Heller, S., Rossetto, L., & Schuldt, H. (2018) · edoc (University of Basel) · doi:10.5281/zenodo.7467932 · Cited by 17
Fast ConvNets Using Group-wise Brain DamagePreprint
Lebedev, V., & Lempitsky, V. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.02515 · Cited by 27
A robust coverless image-synthesized video steganography based on asymmetric structure
Jiao, Y., Zhang, Z., Li, Z., Li, Z., Li, X., & Liu, J. (2024) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2024.104303 · Cited by 18
Quartet-net Learning for Visual Instance Retrieval
Cao, J., Huang, Z. H., Wang, P., et al. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2967262 · Cited by 17
Joint Compression of Near-Duplicate Videos
Wang, H., Tian, T., Ma, M., et al. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2645398 · Cited by 17
Fast content identification in high-dimensional feature spaces using Sparse Ternary Codes
Ferdowsi, S., Voloshynovskiy, S., Kostadinov, D., et al. (2016) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs.2016.7823919 · Cited by 17
Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A second model labelled each claim on 2026-10-08 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review