[1]
Hayat M, Bennamoun M, An S. Deep reconstruction models for image set classification. IEEE Trans Pattern Anal Mach Intell 2015; 37: 713-27.
[2]
Zheng P, Zhao Z-Q, Gao J, Wu X. Image set classification based on cooperative sparse representation. Patt Recogn 2017; 63: 206-17.
[3]
Roy S, Carass A, Prince JL, Pham DL. Subject specific sparse dictionary learning for atlas based brain MRI segmentation. Mach Learn Med Imaging 2014; 8679: 248-55.
[4]
Hayat M, Bennamoun M, An S. Reverse training: An efficient approach for image set classification. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer vision-ECCV 2014 Computer vision-ECCV 2014. Lecture notes in computer science; vol 8694. Springer, Cham 2014.
[5]
Zhang L, Liang Q, Shen Y, Yang M, Liu F. Image set classification based on synthetic examples and reverse training. Neurocomputing 2016; 228: 3-10.
[6]
Hossain MA, Jia X, Benediktsson JA. One-class oriented feature selection and classification of heterogeneous remote sensing images. IEEE J Sel Top Appl Ear Observ Rem Sens 2016; 9: 1606-12.
[7]
Zou Q, Ni L, Zhang T, Wang Q. Deep learning based feature selection for remote sensing scene classification. IEEE Geosci Remote Sens Lett 2015; 12: 2321-5.
[8]
Ibrahim R, Yousri NA, Ismail MA, El-Makky NM. Multi-level gene/MiRNA feature selection using deep belief nets and active learning. Conf Proc IEEE Eng Med Biol Soc 2014; 2014: 3957-60 2014; pp 3957-60.
[9]
Shi T, Zhang C, Li F, Liu W, Huo M. Application of alternating deep belief network in image classification. In: Chinese Control and Decision Conference (CCDC) 2016 Yinchuan, China. 1853-6.
[10]
Murala S, Maheshwari R, Balasubramanian R. Local tetra patterns: A new feature descriptor for content-based image retrieval. IEEE Trans Image Process 2012; 21: 2874-86.
[11]
Wang S, Wu Q, He X, Yang J, Wang Y. Local N-Ary pattern and its extension for texture classification. IEEE Trans Circ Syst Video Tech 2015; 25: 1495-506.
[12]
Chen S, Sanderson C, Harandi MT, Lovell BC. Improved image set classification via joint sparse approximated nearest subspaces.In: IEEE conference on computer vision and pattern recognition. 2013; IEEE, Portland, OR, USA 2013; pp. 452-9.
[13]
Tan H, Ma Z, Zhang S, Zhan Z, Zhang B, Zhang C. Grassmann manifold for nearest points image set classification. Pattern Recognit Lett 2015; 68: 190-6.
[14]
Yuan H, Tang YY. Sparse representation based on set-to-set distance for hyperspectral image classification. IEEE J Select Top Appl Ear Observ Rem Sens 2015; 8: 2464-72.
[15]
Elaiwat S, Bennamoun M, Boussaid F. A semantic RBM-based model for image set classification. Neurocomputing 2016; 205: 507-18.
[16]
Gao S, Zeng Z, Jia K, Chan T-H, Tang J. Patch-set-based representation for alignment-free image set classification IEEE Trans Circ Sys Vid Technol; 26: 1646-58.
[17]
Anitha V, Murugavalli S. Brain tumour classification using two-tier classifier with adaptive segmentation technique. IET Comput Vis 2016; 10: 9-17.
[18]
Huang SY, Hou L, Wu J. MRI-Based electrical property retrieval by applying the Finite-Element Method (FEM). IEEE Trans Microw Theory Tech 2015; 63: 2482-90.
[19]
Subrahmanyam M, Maheshwari R, Balasubramanian R. Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking. Signal Processing 2012; 92: 1467-79.
[20]
Papakostas GA, Koulouriotis DE, Karakasis EG, Tourassis VD. Moment-based local binary patterns: A novel descriptor for invariant pattern recognition applications. Neurocomputin 2013; 99: 358-71.
[21]
Lu J, Wang G, Moulin P. Localized multifeature metric learning for image-set-based face recognition. IEEE Trans Circ Syst Video Tech 2016; 26: 529-40.
[22]
Nanni L, Brahnam S, Lumini A. A simple method for improving local binary patterns by considering non-uniform patterns. Patt Recogn 2012; 45: 3844-52.
[23]
Zhang J, Liang J, Zhao H. Local energy pattern for texture classification using self-adaptive quantization thresholds. IEEE Trans Image Process 2013; 22: 31-42.
[24]
Murala S, Wu QJ. Local ternary co-occurrence patterns: A new feature descriptor for MRI and CT image retrieval. Neurocomputing 2013; 119: 399-412.
[25]
Liu D, Wang S, Huang D, Deng G, Zeng F, Chen H. Medical image classification using spatial adjacent histogram based on adaptive local binary patterns. Comput Biol Med 2016; 72: 185-200.
[26]
Yuan F, Shi J, Xia X, Fang Y, Fang Z, Mei T. High-order local ternary patterns with locality preserving projection for smoke detection and image classification. Inf Sci 2016; 372: 225-40.
[27]
Papa JP, Scheirer W, Cox DD. Fine-tuning deep belief networks using harmony search. Appl Soft Comput 2016; 46: 875-85.
[28]
Tang B, Liu X, Lei J, et al. Deepchart: Combining deep convolutional networks and deep belief networks in chart classification. Signal Processing 2016; 124: 156-61.
[29]
Ma X, Wang H, Geng J, Wang J. Hyperspectral image classification with small training set by deep network and relative distance prior IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2016. IEEE: Beijing, China: pp. 3282-5.
[30]
Jang H, Plis SM, Calhoun VD, Lee J-H. Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks. Neuroimage 2017; 145: 314-28.
[31]
Zhao Z, Jiao L, Zhao J, Gu J, Zhao J. Discriminant deep belief network for high-resolution SAR image classification. Patt Recogn 2017; 61: 686-701.
[34]
Zou Q, Cao Y, Li Q, Huang C, Wang S. Chronological classification of ancient paintings using appearance and shape features. Pattern Recognit Lett 2014; 49: 146-54.