Image Classification with the Fisher Vector: Theory and Practice

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

Facial Landmark Detection with Tweaked Convolutional Neural NetworksPreprint

Wu, Y., Hassner, T., Kim, K., Medioni, G., & Natarajan, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.04031 · Cited by 24

Event Fisher Vectors: Robust Encoding Visual Diversity of Visual Streams

Nagel, M., Mensink, T., & Snoek, C. G. M. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.178 · Cited by 22

Boosted random contextual semantic space based representation for visual recognition

Zhang, C., Xue, Z., Zhu, X., Wang, H., Huang, Q., & Tian, Q. (2016) · Information Sciences · doi:10.1016/j.ins.2016.06.029 · Cited by 23

Toward Good Practices for Fine-Grained Maize Cultivar Identification With Filter-Specific Convolutional Activations

Lü, H., Cao, Z., Xiao, Y., Fang, Z., & Zhu, Y. (2016) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2616485 · Cited by 23

Compressing Fisher Vector for Robust Face Recognition

Wang, H., Hu, J., & Deng, W. (2017) · IEEE Access · doi:10.1109/access.2017.2749331 · Cited by 23

Using Fisher Vector and Bag-of-Audio-Words Representations to Identify Styrian Dialects, Sleepiness, Baby & Orca Sounds

Gosztolya, G. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1726 · Cited by 24

Dense Open-set Recognition with Synthetic Outliers Generated by Real NVP

Grcić, M., Bevandić, P., & Šegvić, S. (2021) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0010260701330143 · Cited by 24

Image Pre-Processing

Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_2 · Cited by 26

A multilevel pooling scheme in convolutional neural networks for texture image recognition

Lyra, L. O., Fabris, A. E., & Florindo, J. B. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111282 · Cited by 23

Hierarchical hybrid statistic based video binary code and its application to face retrieval in TV-series

Li, Y., Wang, R., Shan, S., & Chen, X. (2015) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2015.7163089 · Cited by 23

Basic level scene understanding: categories, attributes and structures

Xiao, J., Hays, J. H., Russell, B. C., Patterson, G., Ehinger, K. A., Torralba, A. B., & Oliva, A. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00506 · Cited by 23

Extracting 3D Layout From a Single Image Using Global Image Structures

Lou, Z., Gevers, T., & Hu, N. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2431443 · Cited by 23

NII-UIT at MediaEval 2015 Affective Impact of Movies Task

Lam, V., Le, S. P., Le, D.-D., Satoh, S., & Duong, D. A. (2015) · MediaEval · Cited by 23

A Large Comparison of Feature-Based Approaches for Buried Target Classification in Forward-Looking Ground-Penetrating Radar

Camilo, J. A., Collins, L. M., & Malof, J. M. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2751461 · Cited by 22

Compressing the Input for CNNs with the First-Order Scattering Transform

Oyallon, E., Belilovsky, E., Zagoruyko, S., & Valko, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_19 · Cited by 23

A hierarchical prototype-based approach for classification

Gu, X., & Ding, W. (2019) · Information Sciences · doi:10.1016/j.ins.2019.07.077 · Cited by 23

2D freehand sketch labeling using CNN and CRF

Zhu, X., Xiao, Y., & Zheng, Y. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08158-z · Cited by 23

Discovering Semantic Vocabularies for Cross-Media Retrieval

Habibian, A., Mensink, T., & Snoek, C. G. M. (2015) · International Conference on Multimedia Retrieval · doi:10.1145/2671188.2749403 · Cited by 22

Unifying Visual Attribute Learning with Object Recognition in a Multiplicative Framework

Liang, K., Chang, H., Ma, B., Shan, S., & Chen, X. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2836461 · Cited by 22

Multiview Semantic Representation for Visual Recognition

Zhang, C., Cheng, J., & Tian, Q. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2875728 · Cited by 22

Ship Detection in SAR Images via Enhanced Nonnegative Sparse Locality-Representation of Fisher Vectors

Wang, X., Li, G., Plaza, A., & He, Y. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3042506 · Cited by 22

GANORCON: Are Generative Models Useful for Few-shot Segmentation?

Saha, O., Cheng, Z., & Maji, S. (2021) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52688.2022.00975 · Cited by 24

DFAEN: Double-order knowledge fusion and attentional encoding network for texture recognition

Yang, Z., Lai, S., Hong, X., Shi, Y., Cheng, Y., & Qing, C. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118223 · Cited by 23

Category co-occurrence modeling for large scale scene recognition

Song, X., Jiang, S., Herranz, L., Kong, Y., & Zheng, K. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.01.019 · Cited by 22

Combining Holistic and Part-based Deep Representations for Computational Painting Categorization

Anwer, R. M., Khan, F. S., van de Weijer, J., & Laaksonen, J. T. (2016) · International Conference on Multimedia Retrieval · doi:10.1145/2911996.2912063 · Cited by 22

Tubelets: Unsupervised Action Proposals from Spatiotemporal Super-Voxels

Jain, M., van Gemert, J. C., Jeǵou, H., Bouthémy, P., & Snoek, C. G. M. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1023-9 · Cited by 22

Computer-aided texture analysis combined with experts' knowledge: Improving endoscopic celiac disease diagnosis

Gadermayr, M., Kogler, H., Karla, M., Merhof, D., Uhl, A., & Vécsei, A. (2016) · World Journal of Gastroenterology · doi:10.3748/wjg.v22.i31.7124 · Cited by 22

Feature Learning and Deep Learning Architecture Survey

Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_10 · Cited by 22

Classification and Regression Using an Outer Approximation Projection-Gradient Method

Barlaud, M., Ali, W. B. H., Combettes, P. L., & Fillatre, L. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2017.2709262 · Cited by 22

Affine Invariant Description and Large-Margin Dimensionality Reduction for Target Detection in Optical Remote Sensing Images

Wan, L., Zheng, L., Huo, H., & Fang, T. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2699329 · Cited by 23

A novel localized and second order feature coding network for image recognition

Chen, B., Li, J., Wei, G., & Ma, B. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.039 · Cited by 22

A generic codebook based approach for gait recognition

Khan, M. H., Farid, M. S., & Grzegorzek, M. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08007-z · Cited by 22

Near-Duplicate Image Detection System Using Coarse-to-Fine Matching Scheme Based on Global and Local CNN Features

Zhou, Z., Lin, K., Cao, Y., Yang, C., & Liu, Y. (2020) · Mathematics · doi:10.3390/math8040644 · Cited by 22

Markov Chain Monte Carlo-Based Bayesian Inference for Learning Finite and Infinite Inverted Beta-Liouville Mixture Models

Bourouis, S., Alroobaea, R., Rubaiee, S., Andejany, M., Almansour, F. M., & Bouguila, N. (2021) · IEEE Access · doi:10.1109/access.2021.3078670 · Cited by 22

Aligning Source Visual and Target Language Domains for Unpaired Video Captioning

Liu, F., Wu, X., You, C., Ge, S., Zou, Y., & Sun, X. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3132229 · Cited by 24

Recognizing locations with Google Glass: A case study

Altwaijry, H., Moghimi, M. K., & Belongie, S. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6836105 · Cited by 21

ISOMER

Sun, C., Burns, B., Nevatia, R., Snoek, C. G. M., Bolles, B., Myers, G., Wang, W., & Yeh, E. (2014) · International Conference on Multimedia Retrieval · doi:10.1145/2578726.2578757 · Cited by 19

A comparative study of fine-grained classification methods in the context of the LifeCLEF plant identification challenge 2015

Champ, J., Lorieul, T., Servajean, M., & Joly, A. (2015) · HAL · Cited by 21

Very deep recurrent convolutional neural network for object recognition

Brahimi, S., Aoun, N. B., & Amar, C. B. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2268672 · Cited by 21

Contextual Bag-of-Words for Robust Visual Tracking

Zeng, F., Ji, Y., & Levine, M. D. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2778561 · Cited by 22

Grad-CAM++ is Equivalent to Grad-CAM With Positive Gradients

Lerma, M., & Lucas, M. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/awjv6348 · Cited by 26

SK‐Unet++: An improved Unet++ network with adaptive receptive fields for automatic segmentation of ultrasound thyroid nodule images

Dai, H., Xie, W., & Xia, E. (2023) · Medical Physics · doi:10.1002/mp.16672 · Cited by 21

Ground-based cloud image categorization using deep convolutional visual features

Ye, L., Cao, Z., Xiao, Y., & Li, W. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351720 · Cited by 21

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

Action Recognition Using Form and Motion Modalities

Meng, Q., Zhu, H., Zhang, W., Piao, X., & Zhang, A. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3350840 · Cited by 21

Towards fine-grained maize tassel flowering status recognition: Dataset, theory and practice

Lü, H., Cao, Z., Xiao, Y., Fang, Z., & Zhu, Y. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.02.026 · Cited by 20

Local phase quantization plus: A principled method for embedding local phase quantization into Fisher vector for blurred image recognition

Xiao, Y., Cao, Z., Wang, L., & Li, T. (2017) · Information Sciences · doi:10.1016/j.ins.2017.08.059 · Cited by 21

Semantic Fisher Scores for Task Transfer: Using Objects to Classify Scenes

Dixit, M., Li, Y., & Vasconcelos, N. M. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2921960 · Cited by 20

Correspondence Attention Transformer: A Context-Sensitive Network for Two-View Correspondence Learning

Ma, J., Wang, Y., Fan, A., Xiao, G., & Chen, R. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3162115 · Cited by 21

Video emotion recognition in the wild based on fusion of multimodal features

Chen, S., Li, X., Jin, Q., Zhang, S., & Qin, Y. (2016) · Proceedings of the 18th ACM International Conference on Multimodal Interaction · doi:10.1145/2993148.2997629 · Cited by 20

The AXES submissions at TrecVid 2013

Aly, R., Arandjelović, R., Chatfield, K., Douze, M., Fernando, B., Harchaoui, Z., McGuinness, K., O'Connor, N. E., Oneaţă, D., Parkhi, O., et al. (2013) · Lirias · Cited by 20

Alone versus In-a-group

Mou, W., Güneş, H., & Patras, I. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2967276 · Cited by 20

Supervised and unsupervised learning process in damage classification of rolling element bearings

Strączkiewicz, M., Czop, P., & Barszcz, T. (2016) · Diagnostyka · Cited by 20

TSNet: Deep Network for Human Action Recognition in Hazy Videos

Chaudhary, S., & Murala, S. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00675 · Cited by 20

Synergy Factorized Bilinear Network with a Dual Suppression Strategy for Brain Tumor Classification in MRI

Xiao, G., Wang, H., Shen, J., Chen, Z., Zhang, Z., & Ge, X. (2021) · Micromachines · doi:10.3390/mi13010015 · Cited by 20

Bag dissimilarity regularized multi-instance learning

Huang, S., Liu, Z., Jin, W., & Mu, Y. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108583 · Cited by 20

Anchor-based knowledge embedding for image aesthetics assessment

Li, L., Zhi, T., Shi, G., Yang, Y., Xu, L., Li, Y., & Guo, Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.03.058 · Cited by 20

Feature Encoding of Spectral Signatures for 3D Non-Rigid Shape Retrieval

Limberger, F. A., & Wilson, R. C. (2015) · British Machine Vision Conference (BMVC) · doi:10.5244/c.29.56 · Cited by 20

Object Categorization Using Class-Specific Representations

Zhang, C., Cheng, J., Li, L., Li, C., & Tian, Q. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2757497 · Cited by 20

Semantically Modeling of Object and Context for Categorization

Zhang, C., Cheng, J., & Tian, Q. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2856096 · Cited by 20

Using the Fisher Vector Representation for Audio-based Emotion Recognition

Gosztolya, G. (2020) · Acta Polytechnica Hungarica · doi:10.12700/aph.17.6.2020.6.1 · Cited by 20

Continuous word embeddings for detecting local text reuses at the semantic level

Zhang, Q., Kang, J., Qian, J., & Huang, X. (2014) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2600428.2609597 · Cited by 19

Object based scene representations using fisher scores of local subspace projections

Dixit, M., & Vasconcelos, N. M. (2016) · neural information processing systems · Cited by 19

HEp-2 cell classification with Vector of Hierarchically Aggregated Residuals

Kastaniotis, D., Fotopoulou, F., Theodorakopoulos, I., Economou, G., & Fotopoulos, S. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.12.013 · Cited by 19

DeepPoint3D: Learning discriminative local descriptors using deep metric learning on 3D point clouds

Srivastava, S., & Lall, B. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.02.027 · Cited by 19

Video Captioning Based on Channel Soft Attention and Semantic Reconstructor

Zhou, L., & Huang, Y. (2021) · Future Internet · doi:10.3390/fi13020055 · Cited by 19

Action recognition using edge trajectories and motion acceleration descriptor

Wang, X., & Qi, C. (2016) · Machine Vision and Applications · doi:10.1007/s00138-016-0746-x · Cited by 19

Isolated sign language recognition using Improved Dense Trajectories

Özdemir, O., Camgöz, N. C., & Akarun, L. (2016) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2016.7496151 · Cited by 19

Gaussian Mixture Model and Deep Neural Network based Vehicle Detection and Classification

Sri, S., & R., K. (2016) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2016.070903 · Cited by 19

Large-scale image retrieval using transductive support vector machines

Çevıkalp, H., Elmas, M., & Özkan, S. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.07.004 · Cited by 19

On Fisher vector encoding of binary features for video face recognition

Martínez-Díaz, Y., Hernández, N., Biscay, R., Chang, L., Méndez-Vázquez, H., & Sucar, L. E. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.01.017 · Cited by 19

Using Interlocutor-Modulated Attention BLSTM to Predict Personality Traits in Small Group Interaction

Lin, Y.-S., & Lee, C.-C. (2018) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3242969.3243001 · Cited by 19

Boosted Convolutional Neural Network for object recognition at large scale

Brahimi, S., Aoun, N. B., & Amar, C. B. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.11.031 · Cited by 20

Improved Very Deep Recurrent Convolutional Neural Network for Object Recognition

Brahimi, S., Aoun, N. B., & Amar, C. B. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00428 · Cited by 19

Assessing Parkinson’s Disease from Speech Using Fisher Vectors

Egas-López, J. V., Orozco‐Arroyave, J. R., & Gosztolya, G. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2217 · Cited by 19

Adaptive Superpixel Segmentation of Marine SAR Images by Aggregating Fisher Vectors

Wang, X., He, Y., Li, G., & Plaza, A. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3051301 · Cited by 19

A Learning Robust and Discriminative Shape Descriptor for Plant Species Identification

Yang, C., Fang, L., Yu, Q., & Wei, H. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3148463 · Cited by 19

Partial 3D Object Retrieval combining Local Shape Descriptors with Global Fisher Vectors

Savelonas, M. A., Pratikakis, I., & Sfikas, K. (2015) · Eurographics · doi:10.2312/3dor.20151051 · Cited by 18

Person re-identification with block sparse recovery

Karanam, S., Li, Y., & Radke, R. J. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.11.015 · Cited by 18

Visual Semantic-Based Representation Learning Using Deep CNNs for Scene Recognition

Gupta, S., Sharma, K., Dinesh, D. A., & Thenkanidiyoor, V. (2021) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3436494 · Cited by 18

Smart integration of sensors, computer vision and knowledge representation for intelligent monitoring and verbal human-computer interaction

Mavropoulos, T., Symeonidis, S., Tsanousa, A., Giannakeris, P., Rousi, M., Kamateri, E., Μεδίτσκος, Γ., Ioannidis, K., Vrochidis, S., & Kompatsiaris, I. Y. (2021) · Journal of Intelligent Information Systems · doi:10.1007/s10844-021-00648-7 · Cited by 19

Patch-NetVLAD+: Learned patch descriptor and weighted matching strategy for place recognition

Cai, Y., Zhao, J., Cui, J., Zhang, F., Feng, T., & Ye, C. (2022) · IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) · doi:10.1109/mfi55806.2022.9913860 · Cited by 18

Visual descriptors for scene categorization: experimental evaluation

Wei, X., Phung, S. L., & Bouzerdoum, A. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-015-9448-4 · Cited by 20

Better Exploiting OS-CNNs for Better Event Recognition in Images

Wang, L., Wang, Z., Guo, S., & Qiao, Y. (2015) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2015.46 · Cited by 19

A 3D descriptor to detect task-oriented grasping points in clothing

Arnau Ramisa, A., Alenyà, G., Moreno-Noguer, F., & Carme Torras, A. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.07.003 · Cited by 19

Generating Video Descriptions with Topic Guidance

Chen, S., Chen, J., & Jin, Q. (2017) · International Conference on Multimedia Retrieval · doi:10.1145/3078971.3079000 · Cited by 18

Review on hierarchical learning methods for large-scale classification task

Hu, Q., Wang, Y., Zhou, Y., Zhao, H., Qian, Y., & Liang, J. (2018) · Scientia Sinica Informationis · doi:10.1360/n112017-00246 · Cited by 18

Multiple Instance Learning for Benign vs. Malignant Classification of Lung Nodules in CT Scans

Safta, W., & Frigui, H. (2018) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit.2018.8642791 · Cited by 18

Alone versus In-a-group

Mou, W., Güneş, H., & Patras, I. (2019) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3321509 · Cited by 20

RGB-D Scene Recognition via Spatial-Related Multi-Modal Feature Learning

Xiong, Z., Yuan, Y., & Wang, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2932080 · Cited by 20

Polyhedral Conic Classifiers for Computer Vision Applications and Open Set Recognition

Çevıkalp, H., & Sağlamlar, H. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2934455 · Cited by 19

Improved Optical Flow for Gesture-based Human-robot Interaction

Chang, J.-Y., Tejero-de-Pablos, A., & Harada, T. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793825 · Cited by 19

On the Impact of Different Fabrication Materials on Fingerprint Presentation Attack Detection

Gonzalez-Soler, L. J., Gomez‐Barrero, M., Chang, L., Pérez‐Suárez, A., & Busch, C. (2019) · International Conference on Biometrics · doi:10.1109/icb45273.2019.8987425 · Cited by 18

Brain programming is immune to adversarial attacks: Towards accurate and robust image classification using symbolic learning

Ibarra-Vázquez, G., Gustavo Olague, A., Chan-Ley, M., Puente, C., & Soubervielle‐Montalvo, C. (2022) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2022.101059 · Cited by 18

Second-order convolutional networks for iris recognition

Jia, L., Shi, X., Sun, Q., Tang, X., & Li, P. (2022) · Applied Intelligence · doi:10.1007/s10489-021-02925-y · Cited by 18

Traffic Scene Classification on a Representation Budget

Sikiric, I., Brkić, K., Bevandić, P., Krešo, I., Krapac, J., & Šegvić, S. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2891995 · Cited by 18

Qualcomm Research and University of Amsterdam at TRECVID 2015: Recognizing Concepts, Objects, and Events in Video

Snoek, C. G. M., Cappallo, S., Fontijne, D., Julian, D., Koelma, D. C., Mettes, P., van de Sande, K. E. A., Sarah, A., Stokman, H. M. G., & Towal, R. B. (2015) · Data Archiving and Networked Services (DANS) · Cited by 17

Multiple Stage Residual Model for Image Classification and Vector Compression

Bai, S., Bai, X., & Liu, W. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2557071 · Cited by 17

Weakly supervised scalable audio content analysis

Kumar, A., & Raj, B. (2016) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2016.7552989 · Cited by 17

Image-Level Structure Recognition Using Image Features, Templates, and Ensemble of Classifiers

Khan, A. H., Chefranov, A. G., & Demirel, H. (2020) · Symmetry · doi:10.3390/sym12071072 · Cited by 19

Author manuscript, published in "International Journal of Computer Vision (2013)" International Journal of Computer Vision manuscript No. (will be inserted by the editor) Image Classification with the Fisher Vector: Theory and Practice

Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J. J. (2013)

International Journal of Computer Vision manuscript No. (will be inserted by the editor) Image Classification with the Fisher Vector: Theory and Practice

Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J. J. (2014)

and Practice

Sánchez, J., Perronnin, F., Mensink, T., Verbeek, J. J., Sánchez, J., & Perronnin, F. (2014)

Local Patch Vectors Encoded by Fisher Vectors for Image Classification

Chen, S., Liu, H., Zeng, X., Qian, S., Wei, W., Wu, G., & Duan, B. (2018) · Information · doi:10.3390/info9020038 · Cited by 7

Fisher Vectors: Beyond Bag-of-Visual-Words Image Representations

Csurka, G., & Perronnin, F. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-25382-9_2 · Cited by 65