Image Classification with the Fisher Vector: Theory and Practice

ImageNet classification with deep convolutional neural networks

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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

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The Pascal Visual Object Classes Challenge: A Retrospective

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Video Google: a text retrieval approach to object matching in videos

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Visual categorization with bags of keypoints

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SUN database: Large-scale scene recognition from abbey to zoo

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Locality-constrained Linear Coding for image classification

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KinePose: A temporally optimized inverse kinematics technique for 6DOF human pose estimation with biomechanical constraints

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Leveraging Deep Embeddings for Explainable Medical Image Analysis

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Boosted Metric Learning for Efficient Identity-Based Face Retrieval

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Aggregating hierarchical binary activations for image retrieval

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Sparse weakly supervised models for object localization in road environment

Zadrija, V., Krapac, J., Šegvić, S., & Verbeek, J. J. (2018) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.10.004 · Cited by 6

High-Order Generalized Orderless Pooling Networks for Synthetic-Aperture Radar Scene Classification

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Improved Image Retrieval Algorithm of GoogLeNet Neural Network

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Spatio-Temporal Representation Matching-Based Open-Set Action Recognition by Joint Learning of Motion and Appearance

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Dermatoscopic image melanoma recognition based on CFLDnet fusion network

Liu, J., Chen, A., Zhou, G., Chen, W., Peng, N. S., & Yan, N. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10920-1 · Cited by 6

Sparse coding and normalization for deep Fisher score representation

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Image Aesthetic Assessment: A Comparative Study of Hand-Crafted & Deep Learning Models

Anwar, A., Kanwal, S., Tahir, M., Saqib, M., Uzair, M., Rahmani, M. K. I., & Ullah, H. (2022) · IEEE Access · doi:10.1109/access.2022.3209196 · Cited by 6

Standardized motion detection and real time heart rate monitoring of aerobics training based on convolution neural network

Chen, W., & Li, M. (2023) · Preventive Medicine · doi:10.1016/j.ypmed.2023.107642 · Cited by 6

Contextual Patch-NetVLAD: Context-Aware Patch Feature Descriptor and Patch Matching Mechanism for Visual Place Recognition

Sun, W., Chen, W., Huang, R., & Tian, J. (2024) · Sensors · doi:10.3390/s24030855 · Cited by 6

Efficient image categorization with sparse Fisher vector

Lu, X., Fang, Z., Xu, T., Zhang, H., & Tuo, H. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178220 · Cited by 6

Fisher Vectors over Random Density Forests for Object Recognition

Baecchi, C., Turchini, F., Seidenari, L., Bagdanov, A. D., & Bimbo, A. D. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.712 · Cited by 6

Exploiting On-Device Image Classification for Energy Efficiency in Ambient-Aware Systems

Shoaib, M., Venkataramani, S., Hua, X., Liu, J., & Li, J. (2015) · Springer eBooks · doi:10.1007/978-3-319-24702-1_7 · Cited by 6

DEEP-CSSR: Scene classification using category-specific salient region with deep features

Qi, M., & Wang, Y. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532517 · Cited by 6

Fine-grained maize cultivar identification using filter-specific convolutional activations

Lü, H., Cao, Z., Xiao, Y., Fang, Z., & Zhu, Y. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7533054 · Cited by 6

An Information Geometry-Based Distance Between High-Dimensional Covariances for Scalable Classification

Wang, Q., Lu, X., Li, P., Gao, Z., & Piao, Y. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2712704 · Cited by 6

A survey on computer vision tools for action recognition, crowd surveillance and suspect retrieval

de Campos, T. E. (2014) · Open Research (University of Surrey) · Cited by 6

Storage Fit Learning with Unlabeled Data

Hou, B., Zhang, L., & Zhou, Z. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/256 · Cited by 6

A Concurrent and Hierarchy Target Learning Architecture for Classification in SAR Application

Touafria, M., & Yang, Q. (2018) · Sensors · doi:10.3390/s18103218 · Cited by 6

Reassessing Hierarchical Representation for Action Recognition in Still Images

Li, R., Liu, Z., & Tan, J. (2018) · IEEE Access · doi:10.1109/access.2018.2872798 · Cited by 6

Subassembly Retrieval of 3D CAD Assembly Models with Different Layout of Components Based on Sinogram

Katayama, K., & Wataru, S. A. T. O. (2019) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2018dap0015 · Cited by 6

GPU-FV

Ma, W., Cao, L., Yu, L., Long, G., & Li, Y. (2016) · International Conference on Multimedia Retrieval · doi:10.1145/2911996.2911997 · Cited by 7

Human action recognition using double discriminative sparsity preserving projections and discriminant ridge-based classifier based on the GDWL-l1 graph

Rahimi, S., Aghagolzadeh, A., & Ezoji, M. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112927 · Cited by 6

An Unsupervised Framework for Online Spatiotemporal Detection of Activities of Daily Living by Hierarchical Activity Models

Negin, F., & Brémond, F. (2019) · Sensors · doi:10.3390/s19194237 · Cited by 6

Self-weighted discriminative metric learning based on deep features for scene recognition

Wang, C., Peng, G., & Lin, W. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08486-0 · Cited by 6

Few-labeled visual recognition for self-driving using multi-view visual-semantic representation

Wang, D., Li, J., & Zhu, S. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.128 · Cited by 6

Artist, Style and Year Classification using Face Recognition and Clustering with Convolutional Neural Networks

Pancaroglu, D. (2020) · doi:10.5121/csit.2020.101604 · Cited by 6

A graph convolutional neural network model with Fisher vector encoding and channel‐wise spatial‐temporal aggregation for skeleton‐based action recognition

Tang, J., Wang, Y., Fu, S., Liu, B., & Liu, W. (2022) · IET Image Processing · doi:10.1049/ipr2.12422 · Cited by 6

Improved Parkinson’s Disease Classification from Diffusion MRI Data by Fisher Vector Descriptors

Salamanca, L., Vlassis, N. A., Diederich, N. J., Bernard, F., & Skupin, A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24571-3_15 · Cited by 6

Discriminative dictionary learning to learn effective features for detecting buried threats in ground penetrating radar data

Malof, J. M., Reichman, D., & Collins, L. M. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2263111 · Cited by 6

Action recognition by Latent Duration Model

Wang, T., Liu, C., & Wang, L.-T. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.07.057 · Cited by 6

Learning Minimal Intra-Genre Multimodal Embedding from Trailer Content and Reactor Expressions for Box Office Prediction

Ko, M.-Y., Li, J.-L., & Lee, C.-C. (2019) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2019.00310 · Cited by 6

Residual Network based Aggregation Model for Skin Lesion ClassificationPreprint

Pan, Y., & Xia, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.09150 · Cited by 6

2nd Place and 2nd Place Solution to Kaggle Landmark Recognition andRetrieval Competition 2019Preprint

Chen, K., Cui, C., Du, Y., Meng, X., & Ren, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.03990 · Cited by 6

Improved Source Counting and Separation for Monaural MixturePreprint

Xiao, Y., & Zhang, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.00175 · Cited by 6

Automatic placental maturity grading via hybrid learning

Lei, B., Tan, E.-L., Chen, S., Li, W., Ni, D., Yao, Y., & Wang, T. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.10.033 · Cited by 5

LoCoRe: Image Re-Ranking with Long-Context Sequence Modeling

Xiao, Z., Šuma, P., Sachdeva, A., Wang, H.-J., Kordopatis-Zilos, G., Tolias, G., & Ordóñez, V. (2025) · 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/cvpr52734.2025.00895 · Cited by 5

Learning Compact Discriminant Representation via Low-Rank Bilinear Pooling

Song, K., Li, H., Cheng, G., Han, J., Nie, F., Gu, B., & Karray, F. O. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3601355 · Cited by 5

Beyond assumptions: A reference architecture to enable unsupervised process discovery from video data

Wördehoff, N., Egger, A., Kratsch, W., König, F., & Röglinger, M. (2025) · Decision Support Systems · doi:10.1016/j.dss.2025.114544 · Cited by 4

Ground-to-Aerial Image Geo-Localization with Cross-View Image Synthesis

Huang, J., & Ye, D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87361-5_34 · Cited by 5

Semantic embedding: scene image classification using scene-specific objects

Parseh, M. J., Rahmanimanesh, M., Keshavarzi, P., & Azimifar, Z. (2022) · Multimedia Systems · doi:10.1007/s00530-022-01010-9 · Cited by 5

Diagnosis of COVID-19 CT Scans Using Convolutional Neural Networks

Chang, V. I. C., Mcwann, S., Hall, K., Xu, Q. A., & Ganatra, M. A. (2024) · SN Computer Science · doi:10.1007/s42979-024-02878-2 · Cited by 5

Learning a discriminative region descriptor for fine-grained cultivar identification

Yang, C., Lyu, W., Yu, Q., Jiang, Y., & Zheng, Z. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109700 · Cited by 5

Automatic Classification of Strain in the Singing Voice Using Machine Learning

Liu, Y., Reddy, M. K., Yagnavajjula, M. K., Räsänen, O. J., Alku, P., Ikävalko, T., Hakanpää, T., Öyry, A., & Laukkanen, A.-M. (2025) · Journal of Voice · doi:10.1016/j.jvoice.2025.03.040 · Cited by 5

Bag of Graphs with Geometric Relationships Among Trajectories for Better Human Action Recognition

Sekma, M., Mejdoub, M., & Amar, C. B. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23231-7_8 · Cited by 5

Sparse Coding Based Fisher Vector Using a Bayesian Approach

Lim, K., & Wang, H. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2636900 · Cited by 5

Rotative maximal pattern: A local coloring descriptor for object classification and recognition

Pang, J., Huang, J., Qin, L., Zhang, W., Qing, L., Huang, Q., & Yin, B. (2017) · Information Sciences · doi:10.1016/j.ins.2017.04.011 · Cited by 5

Sky-CNN: A CNN-based Learning Approach for Skyline Scene Understanding

Sassi, A., Ouarda, W., Amar, C. B., & Miguet, S. (2019) · International Journal of Intelligent Systems and Applications · doi:10.5815/ijisa.2019.04.02 · Cited by 5

Gesture Detection and Recognition Based on Pyramid Frequency Feature Fusion Module and Multiscale Attention in Human-Computer Interaction

Tu, M. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/6043152 · Cited by 5

Automatic multi-gait recognition using pedestrian’s spatiotemporal features

Khan, M. H., Azam, H., & Farid, M. S. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05391-0 · Cited by 5

Towards a Deeper Understanding of Global Covariance Pooling in Deep Learning: An Optimization Perspective

Wang, Q., Zhang, Z., Gao, M., Xie, J., Zhu, P., Li, P., Zuo, W., & Hu, Q. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3321392 · Cited by 6

Learning Robust Point Representation for 3D Non-Rigid Shape Retrieval

Wu, H., Fang, L., Yu, Q., & Yang, C. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3323154 · Cited by 5

Optimizing Food101 Classification with Transfer Learning: A Fine-Tuning Approach Using EfficientNetB0

Philip, A. R. (2024) · International Journal of Intelligent Information Systems · doi:10.11648/j.ijiis.20241304.11 · Cited by 5

Automatic, Real Time, Unsupervised Spatio-temporal 3D Object Detection Using RGB-D Cameras

Alassaf, M. H., Kowsari, K., & Hahn, J. K. (2015) · International Conference on Information Visualisation (IV) · doi:10.1109/iv.2015.80 · Cited by 5

Block-based selection random forest for texture classification using multi-fractal spectrum feature

Zhang, Q., & Xu, Y. (2015) · Neural Computing and Applications · doi:10.1007/s00521-015-1880-5 · Cited by 5

Polynormal Fisher vector for activity recognition from depth sequences

Yang, X., & Tian, Y. (2014) · doi:10.1145/2668956.2668962 · Cited by 5

Selective Encoding for Recognizing Unreliably Localized Faces

Li, A., Morariu, V. I., & Davis, L. S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.412 · Cited by 5

Weakly Supervised Object Localization with Large Fisher Vectors

Krapac, J., & Šegvić, S. (2015) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0005294900440053 · Cited by 5

AT&T Research at TRECVID 2013 : Surveillance Event Detection

Yang, X., Liu, Z., Zavesky, E., Gibbon, D. C., Shahraray, B., & Tian, Y. (2013) · TRECVID · Cited by 5

Weakly-Supervised Semantic Segmentation by Redistributing Region Scores Back to the Pixels

Krapac, J., & Šegvić, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45886-1_31 · Cited by 5

Encoding Multi-resolution Two-Stream CNNs for Action Recognition

Xue, W., Zhao, H., & Zhang, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46675-0_62 · Cited by 5

Saliency-based dense trajectories for action recognition using low-rank matrix decomposition

Wang, X., & Qi, C. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2016.10.015 · Cited by 5

Improving scale invariant feature transform with local color contrastive descriptor for image classification

Guo, S., Huang, W., & Qiao, Y. (2017) · Journal of Electronic Imaging · doi:10.1117/1.jei.26.1.013015 · Cited by 5

Efficient video face recognition by using Fisher Vector encoding of binary features

Martínez-Díaz, Y., Chang, L., Hernández, N., Méndez-Vázquez, H., & Sucar, L. E. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7899839 · Cited by 5

Integrated Global-Local Metric Learning for Person Re-identification

Zhang, J., & Zhao, X. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.72 · Cited by 5

Categorizing scenes by exploring scene part information without constructing explicit models

Bai, S., & Tang, H. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.12.003 · Cited by 5

Increasing Interpretation of Web Topic Detection via Prototype Learning From Sparse Poisson Deconvolution

Pang, J., Hu, A., Huang, Q., Tian, Q., & Yin, B. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2795015 · Cited by 5

Detecting similar sentences using word embedding

Arabacı, M. A., Esen, E., Atar, M. S., Yılmaz, E. H., & Kaltalıoğlu, B. (2018) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2018.8404391 · Cited by 5

Image classification based on log-Euclidean Fisher Vectors for covariance matrix descriptors

Akodad, S., Bombrun, L., Yaacoub, C., Berthoumieu, Y., & Germain, C. (2018) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2018.8608154 · Cited by 5

Person Re-identification Using Group Constraint

Mei, L., Lai, J., Feng, Z., Chen, Z., & Xie, X. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-36189-1_38 · Cited by 5

Design and Implementation of Parking System Using Feature Extraction and Pattern Recognition Technique

Chand, H. V., & Karthikeyan, J. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5285-4_39 · Cited by 5

CLASSIFICATION OF RAILWAY ASSETS IN MOBILE MAPPING POINT CLOUDS

Corongiu, M., Masiero, A., & Tucci, G. (2020) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b1-2020-219-2020 · Cited by 5

Semantics Preserving Hierarchy based Retrieval of Indian heritage monuments

Gupta, R., Mukherjee, P., Lall, B., & Gupta, V. (2020) · Workshop on Structuring and Understanding of Multimedia heritAge Contents (SUMAC) · doi:10.1145/3423323.3423409 · Cited by 5

Comparative evaluation of action recognition methods via riemannian manifolds, fisher vectors and GMMs: Ideal and challenging conditions

Carvajal, J., Wiliem, A., McCool, C., Lovell, B. C., & Sanderson, C. (2016) · QUT ePrints (Queensland University of Technology) · Cited by 5

Effective Skeleton-Based Action Recognition by combining Graph Convolutional Networks and Fisher Vector Encoding

Tang, J., Wang, Y., & Liu, B. (2020) · International Conference on Signal Processing · doi:10.1109/icsp48669.2020.9321006 · Cited by 5

Exploring Misclassification Information for Fine-Grained Image Classification

Wang, D., Zhou, W., Li, J., Yun, W., & Zhu, S. (2021) · Sensors · doi:10.3390/s21124176 · Cited by 5

AABLSTM: A Novel Multi-task Based CNN-RNN Deep Model for Fashion Analysis

Zhang, X., Shen, M., Li, X., & Wang, X. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3519029 · Cited by 5

Unsupervised video segmentation for multi-view daily action recognition

Liu, Z., Wu, Y., Yin, Z., & Gao, C. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104687 · Cited by 5

Sparse Output Coding for Scalable Visual Recognition

Zhao, B., & Xing, E. P. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0839-4 · Cited by 5

Towards Miss Universe automatic prediction: The evening gown competition

Carvajal, J., Wiliem, A., Sanderson, C., & Lovell, B. C. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7899781 · Cited by 5

Accurate 3D bone segmentation in challenging CT images: Bottom-up parsing and contextualized optimization

Lü, L., Wu, D., Lay, N. S., Liu, D., Nogues, I., & Summers, R. M. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477606 · Cited by 5

3D Action Recognition Using Depth-Based Feature and Locality-Constrained Affine Subspace Coding

Liang, C., Chen, E., Qi, L., & Guan, L. (2016) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2016.0058 · Cited by 5

Featured correspondence topic model for semantic search on social image collections

Tu, N. A., Khan, K. U., & Lee, Y.-K. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.01.055 · Cited by 5

Spatiotemporal interest point detector exploiting appearance and motion-variation information

Li, Y., Li, Q., Huang, Q., Xia, R., & Li, X. (2019) · Journal of Electronic Imaging · doi:10.1117/1.jei.28.3.033002 · Cited by 5

Regularized Instance Embedding for Deep Multi-Instance Learning

Lin, Y., & Zhang, H. (2019) · Applied Sciences · doi:10.3390/app10010064 · Cited by 5

Feature encoding with hybrid heterogeneous structure model for image classification

Ji, Z., Yang, Y., Wang, F., Xu, L., & Hu, X. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.0719 · Cited by 5

Learning to Detect Vegetation Using Computer Vision and Low-Cost Cameras

Redolfi, J. A., Felissia, S. F., Bernardi, E., Araguás, G. R., & Flesia, A. G. (2020) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit45562.2020.9067316 · Cited by 5

Comparing endoscopic imaging configurations in computer-aided celiac disease diagnosis

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Empirical Driven Automatic Detection of Lobulation Imaging Signs in Lung CT

Han, G., Liu, X., Soomro, N. Q., Sun, J., Zhao, Y., Zhao, X., & Zhou, C. (2017) · BioMed Research International · doi:10.1155/2017/3842659 · Cited by 5

Tensor decomposition for learning Gaussian mixtures from moments

Khouja, R., Mattei, P., & Bernard Mourrain, C. (2022) · Journal of Symbolic Computation · doi:10.1016/j.jsc.2022.04.002 · Cited by 5

Model-agnostic out-of-distribution detection using combined statistical testsPreprint

Bergamin, F., Mattei, P., Havtorn, J. D., Senetaire, H., Schmutz, H., Maaløe, L., Hauberg, S., & Frellsen, J. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2203.01097 · Cited by 5

Global and Regional Features

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Uncertainty in predictions of Deep Learning models for fine-grained classification

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User Re-Identification Using Clothing Information for Smartphones

Nguyen, M., Puttagunta, R. S., Zhu Li, B., & Derakhshani, R. (2018) · IEEE Conference on Technologies for Homeland Security (HST) · doi:10.1109/ths.2018.8574164 · Cited by 4

Building detection in SAR images based on fusion of classic and deep learning features

Haghiabi, Z., Mokari, N., Arand, B. A., & Imani, M. (2024) · International Journal of Remote Sensing · doi:10.1080/01431161.2024.2347525 · Cited by 4

Enhanced effective convolutional attention network with squeeze-and-excitation inception module for multi-label clinical document classification

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Hybrid descriptor for placental maturity grading

Lei, B., Jiang, F., Zhou, F., Ni, D., Yao, Y., Chen, S., & Wang, T. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08489-x · Cited by 3

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

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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

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

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