Cited by 1,498Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification TechniquesAdvanced Neural Network Applications

The DOI matches its Crossref record; 83 of the 85 references it cites were found in a registry or an index.

Image Classification with the Fisher Vector: Theory and Practice

Open access

Sánchez, Jorge, Perronnin, Florent, Mensink, Thomas, Verbeek, Jakob·2013·International Journal of Computer Vision

DOI 10.1007/s11263-013-0636-x

Open this paper in CoChat — free account Open PDF

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1007/s11263-013-0636-x

Cites

83 of 83 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
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
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
The Pascal Visual Object Classes Challenge: A Retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139
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
Visual categorization with bags of keypoints
Csurka, G. (2004) · Cited by 4,379
SUN database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., & Torralba, A. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539970 · Cited by 3,283
Locality-constrained Linear Coding for image classification
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., & Gong, Y. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5540018 · Cited by 3,100

Show all 85 references

Research works printed but not linked: 1

Clinchant, S., Csurka, G., Perronnin, F., & Renders, J. M. (2007). XRCEs participation to imageval. In ImageEval workshop at CVIR.

Sorted by citations

Items 201–300 of 1,001

SchiNet: Automatic Estimation of Symptoms of Schizophrenia from Facial Behaviour Analysis
Bishay, M., Palasek, P., Priebe, S., & Patras, I. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2907628 · Cited by 76
Self-Supervised Learning for Scene Classification in Remote Sensing: Current State of the Art and Perspectives
Berg, P., Pham, M., & Courty, N. (2022) · Remote Sensing · doi:10.3390/rs14163995 · Cited by 83
Shape feature encoding via Fisher Vector for efficient fall detection in depth-videos
Aslan, M., Şengür, A., Xiao, Y., Wang, H., İnce, M. C., & Ma, X. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2014.12.035 · Cited by 75
A Novel Locally Linear KNN Method With Applications to Visual Recognition
Liu, Q., & Liu, C. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2572204 · Cited by 75
Local Log-Euclidean Multivariate Gaussian Descriptor and Its Application to Image Classification
Li, P., Wang, Q., Zeng, H., & Lei Zhang, A. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2560816 · Cited by 74
Video2vec Embeddings Recognize Events When Examples Are Scarce
Habibian, A., Mensink, T., & Snoek, C. G. M. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2627563 · Cited by 76
Self-citation
Locality in Generic Instance Search from One Example
Ran, T., Gavves, E., Snoek, C. G. M., & Smeulders, A. W. M. (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.2014.269 · Cited by 73
Video-Based Pedestrian Re-Identification by Adaptive Spatio-Temporal Appearance Model
Zhang, W., Ma, B., Liu, K., & Huang, R. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2672440 · Cited by 73
Deep Quantization: Encoding Convolutional Activations with Deep Generative Model
Qiu, Z., Yao, T., & Mei, 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.2017.435 · Cited by 73
Multi-Scale Multi-Feature Context Modeling for Scene Recognition in the Semantic Manifold
Song, X., Jiang, S., & Herranz, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2686017 · Cited by 72
On the generalization of color texture-based face anti-spoofing
Boulkenafet, Z., Komulainen, J., & Hadid, A. (2018) · Image and Vision Computing · doi:10.1016/j.imavis.2018.04.007 · Cited by 72
Fine-Grained Image Classification via Low-Rank Sparse Coding With General and Class-Specific Codebooks
Zhang, C., Liang, C., Li, L., Liu, J., Huang, Q., & Tian, Q. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2545112 · Cited by 72
Discriminative Learning for Alzheimer's Disease Diagnosis via Canonical Correlation Analysis and Multimodal Fusion
Lei, B., Chen, S., Ni, D., & Wang, T. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00077 · Cited by 72
Multi-category EEG signal classification developing time-frequency texture features based Fisher Vector encoding method
Alçіn, Ö. F., Siuly, S., Bajaj, V., Guo, Y., Şengür, A., & Zhang, Y. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.08.050 · Cited by 71
Deep Learning for Coral Classification
Mahmood, A., Bennamoun, M., An, S., Sohel, F. A., Boussaïd, F., Hovey, R. K., Kendrick, G. A., & Fisher, R. B. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-811318-9.00021-1 · Cited by 76
Backpropagated Gradient Representations for Anomaly Detection
Kwon, G., Prabhushankar, M., Temel, D., & AlRegib, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58589-1_13 · Cited by 77
Dirichlet-Based Histogram Feature Transform for Image Classification
Kobayashi, T. (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.2014.413 · Cited by 71
Deep patch learning for weakly supervised object classification and discovery
Tang, P., Wang, X., Huang, Z., Bai, X., & Liu, W. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.05.001 · Cited by 75
Modality and Component Aware Feature Fusion for RGB-D Scene Classification
Wang, A., Cai, J., Lu, J., & Cham, 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.2016.645 · Cited by 71
Deep Scene Image Classification with the MFAFVNet
Li, Y., Dixit, M., & Vasconcelos, N. M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.613 · Cited by 71
Computer Vision – ECCV 2018 Workshops
(2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11018-5 · Cited by 70
Offline Writer Identification Using Convolutional Neural Network Activation Features
Christlein, V., Bernecker, D., Maier, A., & Angelopoulou, E. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24947-6_45 · Cited by 70
A new image feature descriptor for content based image retrieval using scale invariant feature transform and local derivative pattern
Giveki, D., Soltanshahi, M. A., & Montazer, G. A. (2016) · Optik · doi:10.1016/j.ijleo.2016.11.046 · Cited by 70
LG-CNN: From local parts to global discrimination for fine-grained recognition
Xie, G.-S., Zhang, X.-Y., Yang, W., Xu, M., Yan, S., & Liu, C. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.06.002 · Cited by 70
Efficient Action Localization with Approximately Normalized Fisher Vectors
Oneaţă, D., Verbeek, J. J., & Schmid, C. (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.2014.326 · Cited by 67
Self-citation
Few-Shot Learning With Geometric Constraints
Jung, H.-G., & Lee, S. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2957187 · Cited by 69
Edge AR X5: An Edge-Assisted Multi-User Collaborative Framework for Mobile Web Augmented Reality in 5G and Beyond
Ren, P., Qiao, X., Huang, Y., Liu, L., Pu, C., Dustdar, S., & Chen, J. (2020) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2020.3046128 · Cited by 69
Beyond Lesion-Based Diabetic Retinopathy: A Direct Approach for Referral
Pires, R., Avila, S., Jelinek, H. F., Wainer, J., Junior, E. A. D. V., & Rocha, A. D. R. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2498104 · Cited by 69
Interest Point Detector and Feature Descriptor Survey
Krig, S. (2014) · Apress eBooks · doi:10.1007/978-1-4302-5930-5_6 · Cited by 74
Towards Good Practices for Action Video Encoding
Wu, J., Zhang, Y., & Lin, W. (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.2014.330 · Cited by 67
The ImageNet Shuffle: Reorganized Pre-training for Video Event Detection
Mettes, P., Koelma, D. C., & Snoek, C. G. M. (2016) · UvA-DARE (University of Amsterdam) · doi:10.48550/arxiv.1602.07119 · Cited by 68
Heterogeneous hand gesture recognition using 3D dynamic skeletal data
de Smedt, Q., Wannous, H., & Vandeborre, J. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.01.008 · Cited by 68
DeepKSPD: Learning Kernel-Matrix-Based SPD Representation For Fine-Grained Image Recognition
Engin, M., Wang, L., Zhou, L., & Liu, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_38 · Cited by 67
Video Captioning with Guidance of Multimodal Latent Topics
Chen, S., Chen, J., Jin, Q., & Hauptmann, A. G. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123420 · Cited by 69
Scalable Multi-instance Learning
Wei, X.-S., Wu, J., & Zhou, Z. (2014) · Proceedings · doi:10.1109/icdm.2014.16 · Cited by 67
A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification
Yu, H., Yang, W. T., Xia, G.-S., & Liu, G. (2016) · Remote Sensing · doi:10.3390/rs8030259 · Cited by 67
Deep Fisher Kernels -- End to End Learning of the Fisher Kernel GMM Parameters
Sydorov, V., Sakurada, M., & Lampert, C. H. (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.2014.182 · Cited by 71
Synergy of foreground–background images for feature extraction: Offline signature verification using Fisher vector with fused KAZE features
Okawa, M. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.02.027 · Cited by 67
Ship Detection With Superpixel-Level Fisher Vector in High-Resolution SAR Images
Lin, H., Chen, H., Jin, K., Zeng, L., & Yang, J. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2920668 · Cited by 66
Deep feature fusion through adaptive discriminative metric learning for scene recognition
Wang, C., Peng, G., & Bernard De Baets, C. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.05.005 · Cited by 67
The ImageNet Shuffle
Mettes, P., Koelma, D. C., & Snoek, C. G. M. (2016) · International Conference on Multimedia Retrieval · doi:10.1145/2911996.2912036 · Cited by 92
Temporal–Spatial Mapping for Action Recognition
Song, X., Lan, C., Zeng, W., Xing, J., Sun, X., & Yang, J. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2896029 · Cited by 66
Fingerprint Presentation Attack Detection Based on Local Features Encoding for Unknown Attacks
Gonzalez-Soler, L. J., Gomez‐Barrero, M., Chang, L., Pérez‐Suárez, A., & Busch, C. (2021) · IEEE Access · doi:10.1109/access.2020.3048756 · Cited by 63
Multimodal temporal machine learning for Bipolar Disorder and Depression Recognition
Ceccarelli, F., & Mahmoud, M. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-01001-y · Cited by 68
The LEAR submission at Thumos 2014
Oneaţă, D., Verbeek, J. J., & Schmid, C. (2014) · Cited by 68
Self-citation
Example-Based Sketch Segmentation and Labeling Using CRFs
Schneider, R. G., & Tuytelaars, T. (2016) · ACM Transactions on Graphics · doi:10.1145/2898351 · Cited by 62
ProNet: Learning to Propose Object-Specific Boxes for Cascaded Neural Networks
Sun, C., Paluri, M., Collobert, R., Nevatia, R., & Bourdev, L. D. (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.2016.379 · Cited by 64
Unsupervised and Semi-Supervised Image Classification With Weak Semantic Consistency
Zhang, C., Cheng, J., & Tian, Q. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2903628 · Cited by 62
Multi-Level Discriminative Dictionary Learning With Application to Large Scale Image Classification
Shen, L., Sun, G., Huang, Q., Wang, S., Lin, Z., & Wu, E. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2438548 · Cited by 61
3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural NetworksPreprint
Ben-Shabat, Y., Lindenbaum, M., & Fischer, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.08241 · Cited by 61
RAID-G: Robust Estimation of Approximate Infinite Dimensional Gaussian with Application to Material Recognition
Wang, Q., Li, P., Zuo, W., & Zhang, L. (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.2016.480 · Cited by 60
Unsupervised deep hashing through learning soft pseudo label for remote sensing image retrieval
Sun, Y., Ye, Y., Li, X., Feng, S., Zhang, B., Kang, J. S., & Dai, K. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107807 · Cited by 63
Unsupervised Visual Representation Learning by Graph-Based Consistent Constraints
Li, D., Hung, W.-C., Huang, J., Wang, S., Ahuja, N., & Yang, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_41 · Cited by 61
Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization
Ashraf, I., Hur, S., & Park, Y. (2019) · Applied Sciences · doi:10.3390/app9112337 · Cited by 59
Interactive Prototype Learning for Egocentric Action Recognition
Wang, X., Zhu, L., Wang, H., & Yang, Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00806 · Cited by 59
Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition with Image Sets
Wang, W., Wang, R., Huang, Z., Shan, S., & Chen, X. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2746993 · Cited by 59
Metric Learning Based Multimodal Audio-visual Emotion Recognition
Ghaleb, E., Popa, M. C., & Asteriadis, S. (2019) · IEEE Multimedia · doi:10.1109/mmul.2019.2960219 · Cited by 59
Image Classification by Selective Regularized Subspace Learning
Luo, C., Ni, B., Yan, S., & Wang, M. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2495248 · Cited by 59
Ship Detection in SAR Images via Local Contrast of Fisher Vectors
Wang, X., Li, G., Zhang, X.-P. S., & He, Y. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2976880 · Cited by 58
Recognizing an Action Using Its Name: A Knowledge-Based Approach
Gan, C., Yang, Y., Zhu, L., Zhao, D., & Zhuang, Y. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0893-6 · Cited by 58
Evaluation of LBP and Deep Texture Descriptors with a New Robustness Benchmark
Liu, L., Fieguth, P., Wang, X., Pietikäinen, M., & Hu, D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_5 · Cited by 58
Group Re-identification via Unsupervised Transfer of Sparse Features Encoding
Lisanti, G., Martinel, N., Bimbo, A. D., & Foresti, G. L. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.268 · Cited by 58
Automatic Recognition of Facial Displays of Unfelt Emotions
Kulkarni, K., Corneanu, C. A., Ofodile, I., Escalera, S., Baró, X., Hyniewska, S., Allïk, J., & Anbarjafari, G. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2874996 · Cited by 67
Training Faster by Separating Modes of Variation in Batch-Normalized Models
Kalayeh, M. M., & Shah, M. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2895781 · Cited by 58
Estimating mechanical properties of cloth from videos using dense motion trajectories: Human psychophysics and machine learning
Bi, W., Jin, P., Nienborg, H., & Xiao, B. (2018) · Journal of Vision · doi:10.1167/18.5.12 · Cited by 58
Image scene geometry recognition using low-level features fusion at multi-layer deep CNN
Khan, A. H., Chefranov, A. G., & Demirel, H. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.01.085 · Cited by 59
Deep image retrieval: a surveyPreprint
Chen, W., Liu, Y., Wang, W., Bakker, E. M., Georgiou, T., Fieguth, P., Liu, L., & Lew, M. S. (2021) · Leiden Repository (Leiden University) · Cited by 58
Learning spatial-temporal features for video copy detection by the combination of CNN and RNN
Hu, Y., & Lu, X. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.05.013 · Cited by 57
Meta PID Attention Network for Flexible and Efficient Real-World Noisy Image Denoising
Ma, R., Li, S., Zhang, B., & Hu, H. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3150294 · Cited by 57
Hierarchical Coding Vectors for Scene Level Land-Use Classification
Wu, H., Liu, B., Su, W., Zhang, W., & Sun, J. (2016) · Remote Sensing · doi:10.3390/rs8050436 · Cited by 57
Generalized Pooling for Robust Object Tracking
Ma, B., Hu, H., Shen, J., Liu, Y., & Shao, L. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2588329 · Cited by 56
Deep FisherNet for Image Classification
Tang, P., Wang, X., Shi, B., Bai, X., Liu, W., & Tu, Z. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2874657 · Cited by 90
A Methodology for the Detection of Nitrogen Deficiency in Corn Fields Using High-Resolution RGB Imagery
Zermas, D., Nelson, H. J., Stanitsas, P., Morellas, V., MULLA, D. J., & Papanikolopoulos, N. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.3022868 · Cited by 56
Handwritten Word Spotting with Corrected Attributes
Almazán, J., Gordo, A., Fornés, A., & Valveny, E. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.130 · Cited by 56
Fisher and VLAD with FLAIR
van de Sande, K. E. A., Snoek, C. G. M., & Smeulders, A. W. M. (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.2014.304 · Cited by 55
Two-Stream Dictionary Learning Architecture for Action Recognition
Xu, K., Jiang, X., & Sun, T. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2665359 · Cited by 55
Attend and Guide (AG-Net): A Keypoints-Driven Attention-Based Deep Network for Image Recognition
Bera, A., Wharton, Z., Liu, Y., Bessis, N., & Behera, A. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3064256 · Cited by 55
A supervised extreme learning committee for food recognition
Martinel, N., Piciarelli, C., & Micheloni, C. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.01.012 · Cited by 54
Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition
Yang, Z.-X., Tang, L., Zhang, K., & Wong, P. K. (2018) · Cognitive Computation · doi:10.1007/s12559-018-9598-1 · Cited by 55
A review on food recognition technology for health applications
Allegra, D., Battiato, S., Ortis, A., Urso, S., & Polosa, R. (2020) · Health Psychology Research · doi:10.4081/hpr.2020.9297 · Cited by 54
DAGC: Employing Dual Attention and Graph Convolution for Point Cloud based Place Recognition
Sun, Q. L., Liu, H., He, J., Fan, Z., & Du, X. (2020) · International Conference on Multimedia Retrieval · doi:10.1145/3372278.3390693 · Cited by 54
Classifier adaptation at prediction time
Royer, A., & Lampert, C. H. (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.2015.7298746 · Cited by 53
A mid-level video representation based on binary descriptors: A case study for pornography detection
Caetano, C., Avila, S., Schwartz, W. R., Guimarães, S. J. F., & Araújo, A. D. A. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.03.099 · Cited by 53
Efficient Airport Detection Using Line Segment Detector and Fisher Vector Representation
Budak, Ü., Halıcı, U., Şengür, A., Karabatak, M., & Xiao, Y. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2565706 · Cited by 53
Multiple features learning for ship classification in optical imagery
Huang, L., Li, W., Chen, C., Zhang, F., & Lang, H. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4952-y · Cited by 53
Face-Specific Data Augmentation for Unconstrained Face Recognition
Masi, I., Tran, A. T., Hassner, T., Sahin, G., & Medioni, G. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01178-0 · Cited by 55
Disease-Image Specific Generative Adversarial Network for Brain Disease Diagnosis with Incomplete Multi-modal Neuroimages
Pan, Y., Liu, M., Lian, C., Xia, Y., & Shen, D. G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32248-9_16 · Cited by 53
Convolutional Network Features for Scene Recognition
Koskela, M., & Laaksonen, J. T. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2655024 · Cited by 50
One-Shot Learning of Scene Locations via Feature Trajectory Transfer
Kwitt, R., Hegenbart, S., & Niethammer, M. (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.2016.16 · Cited by 51
Action Recognition Using Super Sparse Coding Vector with Spatio-temporal Awareness
Yang, X., & Tian, Y. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10605-2_47 · Cited by 50
Texture Classification Using Dense Micro-Block Difference
Mehta, R., & Egiazarian, K. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2526898 · Cited by 53
Action recognition via spatio-temporal local features: A comprehensive study
Zhen, X., & Shao, L. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.02.006 · Cited by 51
Bioimage Classification with Handcrafted and Learned Features
Nanni, L., Brahnam, S., Ghidoni, S., & Lumini, A. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2821127 · Cited by 49
Fusing Object Semantics and Deep Appearance Features for Scene Recognition
Sun, N., Li, W., Liu, J., Han, G., & Wu, C. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2848543 · Cited by 49
Visual content-based web page categorization with deep transfer learning and metric learning
López-Sánchez, D., Arrieta, A. G., & Corchado, J. M. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.08.086 · Cited by 53
Classification of Tree Species and Standing Dead Trees with Lidar Point Clouds Using Two Deep Neural Networks: PointCNN and 3DmFV-Net
Hell, M., Brandmeier, M., Briechle, S., & Krzystek, P. (2022) · PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science · doi:10.1007/s41064-022-00200-4 · Cited by 50
Event-based media processing and analysis: A survey of the literature
Tzelepis, C., Ma, Z., Mezaris, V., Ionescu, B., Kompatsiaris, I., Boato, G., Sebe, N., & Yan, S. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.05.005 · Cited by 49
Hybrid dermoscopy image classification framework based on deep convolutional neural network and Fisher vector
Yu, Z. T., Ni, D., Chen, S., Qin, J., Li, S., Wang, T., & Lei, B. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950524 · Cited by 50
Coarse-to-Fine: A RNN-Based Hierarchical Attention Model for Vehicle Re-identification
Wei, X.-S., Zhang, C.-L., Liu, L., Shen, C., & Wu, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20890-5_37 · Cited by 49
Convolutional descriptors aggregation via cross-net for skin lesion recognition
Yu, Z. T., Jiang, F., Zhou, F., He, X., Ni, D., Chen, S., Wang, T., & Lei, B. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106281 · Cited by 50

Authors: Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J.
Affiliations: Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
Xerox (France) (FR)
University of Amsterdam (NL)
Institut national de recherche en sciences et technologies du numérique (FR)
Corresponding author: Sánchez, Jorge
ORCID: Sánchez, Jorge
Mensink, Thomas
Verbeek, Jakob
Venue: International Journal of Computer Vision
Publisher: Springer Science and Business Media LLC
Published: 2013-06-12
Volume: 105
Issue: 3
Pages: 222-245
ISSN: 0920-5691 (ISSN-L)
1573-1405
DOI: 10.1007/s11263-013-0636-x
OpenAlex: W1966385142
Cited by: 1,498
References: 85
Citations by year: 2026: 11 · 2025: 20 · 2024: 45 · 2023: 58 · 2022: 102 · 2021: 95 · 2020: 128 · 2019: 164 · 2018: 167 · 2017: 211 · 2016: 224 · 2015: 167 · 2014: 91 · 2013: 13
FWCI: 171.88
Type: journal article
Language: en
Open access: green
handle.uba.uva.nl
Licence: www.springer.com/tdm (tdm)
Locations: International Journal of Computer Vision (publishedVersion)
UvA-DARE (University of Amsterdam) (publishedVersion)
UvA-DARE (University of Amsterdam) (submittedVersion)
HAL (submittedVersion, other-oa)
Data Archiving and Networked Services (DANS) (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 Neural Network Applications
Keywords: Fisher vectors, Fisher kernel, image classification, bag of visual words, product quantization, image retrieval, feature encoding, linear classifiers

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.

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

Image Classification with the Fisher Vector: Theory and Practice

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks