A novel feature extraction method for scene recognition based on Centered Convolutional Restricted Boltzmann Machines

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 · see citing works

Histograms of Oriented Gradients for Human Detection

Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works

A fast learning algorithm for deep belief nets

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336 · see citing works

Learning Deep Architectures for AI

Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,048 · see citing works

A performance evaluation of local descriptors

Mikolajczyk, K., & Schmid, C. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.188 · Cited by 6,723 · see citing works

Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope

Oliva, A., & Torralba, A. B. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011139631724 · Cited by 6,321 · see citing works

Learning Deep Architectures for AI

Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,037 · see citing works

Training Products of Experts by Minimizing Contrastive Divergence

Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 1× · see citing works

A Bayesian Hierarchical Model for Learning Natural Scene Categories

Fei-Fei, L., & Perona, P. (2005) · 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.2005.16 · Cited by 3,657 · see citing works

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 · see citing works

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 · see citing works

Learning Hierarchical Features for Scene Labeling

Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.231 · Cited by 2,620 · see citing works

Untitled work

(2014) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 2,534 · see citing works

Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories

Fei-Fei, L., Fergus, R., & Perona, P. (2005) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvpr.2004.383 · Cited by 2,513 · see citing works

Deep learning for visual understanding: A review

Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.09.116 · Cited by 2,504 · see citing works

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553453 · Cited by 2,412 · see citing works

Neural Networks: Tricks of the Trade: Second Edition

(2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8 · Cited by 2,116 · see citing works

Deep learning for detecting robotic grasps

Lenz, I., Lee, H., & Saxena, A. (2013) · Int. J. Robotics Res. · doi:10.1177/0278364914549607 · Cited by 1,820 · see citing works

Recognizing indoor scenes

Quattoni, A., & Torralba, A. B. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206537 · Cited by 1,533 · see citing works

Image Classification with the Fisher Vector: Theory and Practice

Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0636-x · Cited by 1,498 · see citing works

Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition

Ranzato, M., Huang, F., Boureau, Y.-L., & LeCun, Y. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383157 · Cited by 1,186 · see citing works

SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition

Zhang, H., Berg, A. C., Maire, M., & Malik, J. (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.2006.301 · Cited by 1,128 · see citing works

Object Detectors Emerge in Deep Scene CNNsPreprint

Zhou, B., Khosla, A., Lapedriza, À., Oliva, A., & Torralba, A. B. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.6856 · Cited by 823 · see citing works

Modeling Human Motion Using Binary Latent Variables

Taylor, G. W., Hinton, G. E., & Roweis, S. T. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0173 · Cited by 708 · see citing works

Multi-spectral SIFT for scene category recognition

Brown, M., & Susstrunk, S. E. (2011) · doi:10.1109/cvpr.2011.5995637 · Cited by 509 · see citing works

Scene recognition and weakly supervised object localization with deformable part-based models

Pandey, M., & Lazebnik, S. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126383 · Cited by 412 · see citing works

Unsupervised learning of hierarchical representations with convolutional deep belief networks

Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2011) · Communications of the ACM · doi:10.1145/2001269.2001295 · Cited by 386 · see citing works

Evaluation of color descriptors for object and scene recognition

van de Sande, K. E. A., Gevers, T., & Snoek, C. G. M. (2008) · 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.2008.4587658 · Cited by 235 · see citing works

Recognizing indoor scenes

Quattoni, A., & Torralba, A. B. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvprw.2009.5206537 · Cited by 227 · see citing works

Modeling pixel means and covariances using factorized third-order boltzmann machines

Ranzato, M., & Hinton, G. E. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539962 · Cited by 215 · see citing works

Image classification by non-negative sparse coding, low-rank and sparse decomposition

Zhang, C., Liu, J., Tian, Q., Xu, C., Lu, H., & Ma, S. (2011) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2011.5995484 · Cited by 188 · see citing works

Stacks of convolutional Restricted Boltzmann Machines for shift-invariant feature learning

Norouzi, M., Ranjbar, M., & Mori, G. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206577 · Cited by 186 · see citing works

Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images

Ranzato, M., Krizhevsky, A., & Hinton, G. E. (2010) · Cited by 182 · see citing works

Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling

Kae, A., Sohn, K., Lee, H., & Miller, E. G. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.263 · Cited by 176 · see citing works

A Better Way to Pretrain Deep Boltzmann Machines

Hinton, G. E., & Salakhutdinov, R. (2012) · neural information processing systems · Cited by 112 · see citing works

Latent Pyramidal Regions for Recognizing Scenes

Sadeghi, F., & Tappen, M. F. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33715-4_17 · Cited by 84 · see citing works

Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines

Cho, K., Raiko, T., & Ihler, A. (2011) · Cited by 78 · see citing works

Regional deep learning model for visual tracking

Wu, G., Lu, W., Gao, G., Zhao, C., & Liu, J. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.10.064 · Cited by 78 · see citing works

Stacks of convolutional Restricted Boltzmann Machines for shift-invariant feature learning

Norouzi, M., Ranjbar, M. A., & Mori, G. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvprw.2009.5206577 · Cited by 45 · see citing works

Learning Hybrid Part Filters for Scene Recognition

Zheng, Y., Jiang, Y., & Xue, X. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33715-4_13 · Cited by 41 · see citing works

A Two Level Approach for Scene Recognition

Lü, L., Toyama, K., & Hager, G. D. (2005) · 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.2005.51 · Cited by 38 · see citing works

Scene and place recognition using a hierarchical latent topic model

Yang, J., Zhang, S., Wang, G., & Li, M. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.07.005 · Cited by 14 · see citing works

Centering SVDD for Unsupervised Feature Representation in Object Classification

Wang, D., & Tan, X. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-42051-1_47 · Cited by 7 · see citing works

Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges

Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.056 · Cited by 930 · see citing works

Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions

Nweke, H. F., Wah, T. Y., Mujtaba, G., & Al-Garadi, M. A. (2018) · Information Fusion · doi:10.1016/j.inffus.2018.06.002 · Cited by 400 · see citing works

Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree

Azam, Z., Islam, M. M., & Huda, M. N. (2023) · IEEE Access · doi:10.1109/access.2023.3296444 · Cited by 265 · see citing works

Deep learning based human activity recognition (HAR) using wearable sensor data

Gupta, S. (2021) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2021.100046 · Cited by 225 · see citing works

MDFN: Multi-scale deep feature learning network for object detection

Ma, W., Wu, Y., Cen, F., & Wang, G. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.107149 · Cited by 161 · see citing works

A novel scene classification model combining ResNet based transfer learning and data augmentation with a filter

Liu, S., Tian, G., & Xu, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.01.090 · Cited by 147 · see citing works

Review of classical dimensionality reduction and sample selection methods for large-scale data processing

Xu, X., Liang, T., Zhu, J., Zheng, D., & Sun, T. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.02.100 · Cited by 146 · see citing works

Adversarially Approximated Autoencoder for Image Generation and Manipulation

Xu, W., Keshmiri, S., & Wang, G. (2019) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2019.2898777 · Cited by 81 · see citing works

Classification, Prediction, and Monitoring of Parkinson’s disease using Computer Assisted Technologies: A Comparative Analysis

Goyal, J., Khandnor, P., & Aseri, T. C. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103955 · Cited by 60 · see citing works

A self-organizing deep belief network for nonlinear system modeling

Qiao, J., Wang, G., Li, X., & Li, W. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.01.019 · Cited by 60 · see citing works

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 50 · see citing works

Restricted Boltzmann Machines With Gaussian Visible Units Guided by Pairwise Constraints

Chu, J., Wang, H., Meng, H., Jin, P., & Li, T. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2018.2863601 · Cited by 46 · see citing works

Object Detection with Convolutional Neural Networks

Li, K., Ma, W., Sajid, U., Wu, Y., & Wang, G. (2020) · CRC Press eBooks · doi:10.1201/9781351003827-2 · Cited by 42 · see citing works

A new deep belief network based on RBM with glial chains

Geng, Z., Li, Z., & Han, Y. M. (2018) · Information Sciences · doi:10.1016/j.ins.2018.06.043 · Cited by 40 · see citing works

Boosting Occluded Image Classification via Subspace Decomposition-Based Estimation of Deep Features

Cen, F., & Wang, G. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2931067 · Cited by 32 · see citing works

Leveraging Wearable Sensors for Human Daily Activity Recognition with Stacked Denoising Autoencoders

Ni, Q., Fan, Z., Zhang, L., Nugent, C., Cleland, I., Zhang, Y., & Zhou, N. Q. (2020) · Sensors · doi:10.3390/s20185114 · Cited by 32 · see citing works

Fuzzy Removing Redundancy Restricted Boltzmann Machine: improving learning speed and classification accuracy

Lü, X., Meng, L., Chen, C., & Wang, P. (2019) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2019.2940415 · Cited by 25 · see citing works

Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM

Chu, J., Wang, H., Liu, J., Gong, Z., & Li, T. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3015959 · Cited by 24 · see citing works

Scene Recognition from Image Using Convolutional Neural Network

Masood, S., Ahsan, U., Munawwar, F., Rizvi, D. R., & Ahmed, M. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.03.400 · Cited by 26 · see citing works

Plug-and-Play Rescaling Based Crowd Counting in Static Images

Sajid, U., & Wang, G. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093561 · Cited by 20 · see citing works

Predicting the quality of online health expert question-answering services with temporal features in a deep learning framework

Hu, Z., Zhang, Z., Yang, H., Chen, Q., Zhu, R., & Zuo, D. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.11.039 · Cited by 18 · see citing works

An echo state network based approach to room classification of office buildings

Shi, G., Zhao, B., Li, C., Wei, Q., & Liu, D. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.12.033 · Cited by 18 · see citing works

Dynamic Scene Recognition Using Spatiotemporal Based DLTP on Spark

Uddin, M. A., Akhond, M. R., & Lee, Y.-K. (2018) · IEEE Access · doi:10.1109/access.2018.2878865 · Cited by 12 · see citing works

An Optimization-Linked Intelligent Security Algorithm for Smart Healthcare Organizations

Irshad, R. R., Alattab, A. A., Alsaiari, O. A. S., Sohail, S. S., Aziz, A., Madsen, D. Ø., & Alalayah, K. M. A. (2023) · Healthcare · doi:10.3390/healthcare11040580 · Cited by 10 · see citing works

A Centered Convolutional Restricted Boltzmann Machine Optimized by Hybrid Atom Search Arithmetic Optimization Algorithm for Sentimental Analysis

Karthik, E., & Sethukarasi, T. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-10797-7 · Cited by 10 · see citing works

Incept_LSTM : Accession for human activity concession in automatic surveillance

Girdhar, P., Johri, P., & Virmani, D. (2020) · Journal of Discrete Mathematical Sciences and Cryptography · doi:10.1080/09720529.2020.1804132 · Cited by 9 · see citing works

An efficient attention-based hybridized deep learning network with deep RBM features for customer behavior prediction in digital marketing

Sakthi, B. T. T. S., & Sundar, D. (2024) · Kybernetes · doi:10.1108/k-03-2024-0837 · Cited by 8 · see citing works

Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit AssignmentPreprint

Ororbia, A. G. (2023) · doi:10.31219/osf.io/xukqf · Cited by 10 · see citing works

Efficient Deep Learning Approach for Multi-label Semantic Scene Classification

Senthilkumar, D., Akshayaa, C., & Washington, D. G. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-51859-2_37 · Cited by 6 · see citing works

Enhanced channel allocation scheme for cross layer management in wireless network based on interference management

Rukmani, K. V., & Nagarajan, N. (2018) · Cluster Computing · doi:10.1007/s10586-017-1596-7 · Cited by 5 · see citing works

A Novel Multi-Modal Network-Based Dynamic Scene Understanding

Uddin, M. A., Joolee, J. B., Lee, Y.-K., & Sohn, K.-A. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3462218 · Cited by 3 · see citing works

A real time optical sensing-based image processing and deep learning method for 2D-3D human activity classification for virtual try-on systems

Mrinali, A., & Gupta, P. (2025) · Journal of Optics · doi:10.1007/s12596-025-02753-0 · Cited by 1 · see citing works

An IoT-based healthcare system using blockchain technology and multiscale stacked Residual-GRU for secure data transmission

Krishnan, A., Arunachalam, R., Kosuru, S. N. V. J. D., Swapna, T., Venugopal, S., & Yogapriya, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-28856-z · Cited by 1 · see citing works

Image Classification Based on Modified BOW Model

Zhang, G., Yang, J., Zhang, S., & Yang, F. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-38771-0_33 · Cited by 1 · see citing works

Generative and discriminative infinite restricted Boltzmann machine training

Wang, Q., Gao, X., Wan, K., & Hu, Z. (2022) · International Journal of Intelligent Systems · doi:10.1002/int.22908 · Cited by 1 · see citing works

A Deep Unsupervised Representation Learning Architecture using Coefficient of Variation in Hidden Layers

Ding, X., Chu, J., Hu, J., Yu, H., & Li, T. (2023) · IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) · doi:10.1109/ccis59572.2023.10263220 · Cited by 1 · see citing works

Model for recognizing human behavior via feature and classifier selection

Yanamadni, V. R., Karthikeya, T., Reddy, R. S., Gowtham, S., Goud, S. K., Reddy, G. V., & Kotov, E. V. (2024) · MATEC Web of Conferences · doi:10.1051/matecconf/202439201092 · Cited by 1 · see citing works

Self-learning Local Supervision Encoding Framework to Constrict and Disperse Feature Distribution for Clustering.Preprint

Chu, J., Li, T., Wang, H., Liu, J., & Meng, H. (2018) · arXiv (Cornell University) · Cited by 1 · see citing works

Unsupervised Feature Learning Architecture with Multi-clustering Integration RBMPreprint

Chu, J., Wang, H., Liu, J., Gong, Z., & Li, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.01967 · Cited by 1 · see citing works

An echo state network based on Levenberg-Marquardt algorithm

Wang, L., Yang, C., Qiao, J., & Wang, G. (2017) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2017.8027966

An embarrassingly simple comparison of machine learning algorithms for indoor scene classificationPreprint

Gamage, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.12261

Identifying Scenes through Deep Features and Places Dataset Integration

Singh, A., Bansal, A., Shiwangi, B., Singh, A. K., Verma, R., & Kumar, S. (2025) · International Conference on Computing, Communication and Automation · doi:10.1109/iccca66364.2025.11325362

Effective Feature Representation and Attention-based Dense LSTM for Human Brain Activity Recognition using EEG Signals during Yoga and Meditation

Ramaraju, B., Janapati, R., & Kollem, S. (2025) · International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) · doi:10.1109/icaiihi67124.2025.11403078

Intuitive Multi‐Scale Visual Feature Fusion for Automated Classification of Human Activity

Gautam, V., Kaur, G., SINHA, A., Verma, A. K., & Nag, A. (2026) · IET Cyber-Physical Systems Theory & Applications · doi:10.1049/cps2.70054

Stacks of convolutional Restricted Boltzmann Machines for shift-invariant feature learning

Norouzi, M., Ranjbar, M., & Mori, G. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206577 · Cited by 186 · see citing works

Stacks of convolutional restricted Boltzmann machines for shift-invariant feature learning. CVPR

Norouzi, M., Ranjbar, M., & Mori, G. (2009)

Convolutional restricted boltzmann machines for feature learning

Norouzi, M. (2009) · Summit (Simon Fraser University) · Cited by 11 · see citing works

Complementary Convolutional Restricted Boltzmann Machine and Its Applications in Image Recognition

Zhang, J. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-8991-9_1

Gaussian-Bernoulli Based Convolutional Restricted Boltzmann Machine for Images Feature Extraction

Li, Z., Cai, X., & Liang, T. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46672-9_66 · Cited by 2 · see citing works