[1] Krishna R, Zhu Y K, Groth O, Johnson J, Hata K, Kravitz J, et al. Visual genome: Connecting language and vision using crowdsourced dense image annotations. International Journal of Computer Vision, 2017, 123(1): 32-73 http://dl.acm.org/citation.cfm?id=3089101
[2] Pietikäinen M, Hadid A, Zhao G Y, Ahonen T. Computer Vision Using Local Binary Patterns. London: Springer Berlin, 2011.
[3] Oliva A, Torralba A. Building the gist of a scene: The role of global image features in recognition. Progress in Brain Research, 2006, 155: 23-36 doi: 10.1016/S0079-6123(06)55002-2
[4] Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110 http://dl.acm.org/citation.cfm?id=996342&CFID=520673225&CFTOKEN=77943935
[5] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3rd International Conference on Learning Representations. San Diego, CA, USA, 2015. 1-14
[6] Chandra B, Gupta M. An efficient statistical feature selection approach for classification of gene expression data. Journal of Biomedical Informatics, 2011, 44(4): 529-535 http://dl.acm.org/citation.cfm?id=2010675
[7] Farhadi A, Endres I, Hoiem D, David F. Describing objects by their attributes. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009: 1778-1785 doi: 10.1109/CVPRW.2009.5206772
[8] Kumar N, Belhumeur P, Nayar S. FaceTracer: A search engine for large collections of images with faces. In: Proceedings of the 10th European Conference on Computer Vision. Marseille, France: Springer, 2008. 340-353
[9] Kumar N, Berg A C, Belhumeur P N, Nayar S K. Attribute and simile classifiers for face verification. In: Proceedings of the 12th International Conference on Computer Vision. Kyoto, Japan: IEEE, 2009. 365-372
[10] Jayaraman D, Grauman K. Zero-shot recognition with unreliable attributes. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Quebec, Canada: MIT Press, 2014. 3464-3472
[11] Berg T L, Berg A C, Shih J. Automatic attribute discovery and characterization from noisy web data. In: Proceedings of the 11th European Conference on Computer Vision. Heraklion, Greece: Springer, 2010. 663-676
[12] Gan C, Yang T B, Gong B Q. Learning attributes equals multi-source domain generalization. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 87-97
[13] Liu Z W, Luo P, Wang X G, Tang X O. Deep learning face attributes in the wild. In: Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015. 3730-3738
[14] Tang P, Zhang J, Wang X G, Feng B, Roli F B, Liu W Y. Learning extremely shared middle-level image representation for scene classification. Knowledge and Information Systems, 2017, 52(2): 509-530 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=414a554cd565e9f2c284abfe28edbdd1
[15] Bradley C, Boult T E, Ventura J. Cross-modal facial attribute recognition with geometric features. In: Proceedings of the 12th IEEE International Conference on Automatic Face & Gesture Recognition. Washington, USA: IEEE, 2017. 891-896
[16] Liu Z W, Luo P, Qiu S, Wang X G, Tang X O. DeepFashion: Powering robust clothes recognition and retrieval with rich annotations. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 1096-1104
[17] Qi G J, Hua X S, Rui Y, Tang J H, Mei T, Zhang H J. Correlative multi-label video annotation. In: Proceedings of the 15th ACM International Conference on Multimedia. Augsburg, Germany: ACM, 2007. 17-26
[18] Parikh D, Grauman K. Relative attributes. In: Proceedings of the 2011 International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011. 503-510
[19] Kovashka A, Grauman K. Attribute adaptation for personalized image search. In: Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney, Australia: IEEE, 2013. 3432-3439
[20] Kovashka A, Parikh D, Grauman K. WhittleSearch: Interactive image search with relative attribute feedback. International Journal of Computer Vision, 2015, 115(2): 185-210 http://d.wanfangdata.com.cn/periodical/613be5470b6e7be562fc6e807806b4d8
[21] Yu A, Grauman K. Just noticeable differences in visual attributes. In: Proceedings of the 2015 International Conference on Computer Vision. Santiago, Chile: IEEE, 2015. 2416-2424
[22] Cheng Y H, Qiao X, Wang X S, Yu Q. Random forest classifier for zero-shot learning based on relative attribute. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(5): 1662-1674 doi: 10.1109/TNNLS.2017.2677441
[23] Yuan B D, Tu J, Zhao R W, ZhengY B, Jiang Y G. Learning part-based mid-level representation for visual recognition. Neurocomputing, 2018, 275: 2126-2136 doi: 10.1016/j.neucom.2017.10.062
[24] Liu X, Wang J, Wen S L, Ding E R, Lin Y Q. Localizing by describing: Attribute-guided attention localization for fine-grained recognition. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence. San Francisco, California, USA: AAAI, 2017. 4190-4196
[25] Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, et al. Photo-realistic single image super-resolution using a generative adversarial network[Online], available: https://arxiv.org/abs/1609.04802, September 15, 2016.
[26] Singh K K, Lee Y J. End-to-end localization and ranking for relative attributes. In: Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016. 753-769
[27] Liu X G, Yu Y Z, Shum H Y. Synthesizing bidirectional texture functions for real-world surfaces. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques. New York, USA: ACM, 2001. 97-106
[28] Leung T, Malik J. Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision, 2001, 43(1): 29-44 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=d84bc2da159ff99e8d8f4c67b6af5546
[29] Varma M, Zisserman A. A statistical approach to texture classification from single images. International Journal of Computer Vision, 2005, 62(1-2): 61-81 doi: 10.1007/s11263-005-4635-4
[30] Heera M M, Divya J K, Varma M S, Divya R A, Agrawal D V K. Minimum variance optimal filter design for a 3x3 MEMS gyroscope cluster configuration. IFAC-Papersonline, 2016, 49(1): 639-645 doi: 10.1016/j.ifacol.2016.03.128
[31] Sharan L, Rosenholtz R, Adelson E. Material perception: What can you see in a brief glance? Journal of Vision, 2009, 9(8): 784
[32] Liu C, Sharan L, Adelson E H, Rosenholtz R. Exploring features in a Bayesian framework for material recognition. In: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE, 2010. 239-246
[33] Hu D E, Bo L F, Ren X F. Toward robust material recognition for everyday objects. In: Proceedings of the 2011 British Machine Vision Conference. Dundee, UK: BMVA Press, 2011. 1-11
[34] Sharan L, Liu C, Rosenholtz R, Adelson E H. Recognizing materials using perceptually inspired features. International Journal of Computer Vision, 2013, 103(3): 348-371 doi: 10.1007/s11263-013-0609-0
[35] Kampouris C, Zafeiriou S, Ghosh A, Malassiotis S. Fine-grained material classification using micro-geometry and reflectance. In: Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016. 778-792
[36] Dong J Y, Chantler M. Capture and synthesis of 3D surface texture. International Journal of Computer Vision, 2005, 62(1-2): 177-194 doi: 10.1007/s11263-005-4641-6
[37] Jian M W, Yin Y L, Dong J Y, Zhang W Y. Comprehensive assessment of non-uniform illumination for 3D heightmap reconstruction in outdoor environments. Computers in Industry, 2018, 99: 110-118 doi: 10.1016/j.compind.2018.03.034
[38] Jian M W, Dong J Y. Capture and fusion of 3d surface texture. Multimedia Tools and Applications, 2011, 53(1): 237-251 doi: 10.1007/s11042-010-0509-z
[39] Jian M W, Lam K M, Dong J Y. Illumination-insensitive texture discrimination based on illumination compensation and enhancement. Information Sciences, 2014, 269: 60-72 doi: 10.1016/j.ins.2014.01.019
[40] Van Der Maaten L, Hinton G. Visualizing data using t-SNE. Journal of Machine Learning Research, 2008, 9(11): 2579-2605 http://www.mendeley.com/catalog/visualizing-data-using-tsne/
[41] Friedman J H. Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 2001, 29(5): 1189-1232 http://bioscience.oxfordjournals.org/external-ref?access_num=10.1214/aos/1013203451&link_type=DOI
[42] 侯杰, 茅耀斌, 孙金生.基于指数损失和0-1损失的在线Boosting算法.自动化学报, 2014, 40(4): 635-642 doi: 10.3724/SP.J.1004.2014.00635

Hou Jie, Mao Yao-Bin, Sun Jin-Sheng. Online boosting algorithms based on exponential and 0-1 loss. Acta Automatica Sinica, 2014, 40(4): 635-642 doi: 10.3724/SP.J.1004.2014.00635
[43] Vu H T, Gallinari P. Using RankBoost to compare retrieval systems. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management. Bremen, Germany: ACM, 2005. 309-310
[44] Chen T Q, Guestrin C. XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, USA: ACM, 2016. 785-794
[45] Feng J, Yu Y, Zhou Z H. Multi-layered gradient boosting decision trees. In: Proceedings of the 32nd Conference on Neural Information Processing Systems. Montréal, Canada: 2018.
[46] Vedaldi A, Gulshan V, Varma M, Zisserman A. Multiple kernels for object detection. In: Proceedings of the 2009 IEEE 12th International Conference on Computer Vision. Kyoto, Japan: IEEE, 2009. 606-613
[47] Xia H, Hoi S C H. MKBoost: A framework of multiple kernel boosting. IEEE Transactions on Knowledge and Data Engineer, 2013, 25(7): 1574-1586 doi: 10.1109/TKDE.2012.89
[48] Zhang Z Y, Lyons M, Schuster M, Akamatsu S. Comparison between geometry-based and gabor-wavelets-based facial expression recognition using multi-layer perceptron. In: Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition. Nara, Japan: IEEE, 1998. 454-459
[49] Bai S, Sun S Y, Bai X, Zhang Z X, Tian Q. Smooth neighborhood structure mining on multiple affinity graphs with applications to context-sensitive similarity. In: Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016. 592-608
[50] 徐丹蕾, 杜兰, 刘宏伟, 洪灵, 李彦兵.一种基于变分相关向量机的特征选择和分类结合方法.自动化学报, 2011, 37(8): 932-943 doi: 10.3724/SP.J.1004.2011.00932

Xu Dan-Lei, Du Lan, Liu Hong-Wei, Hong Ling, Li Yan-Bing. Joint feature selection and classification design based on variational relevance vector machine. Acta Automatica Sinica, 2011, 37(8): 932-943 doi: 10.3724/SP.J.1004.2011.00932
[51] Liu Z Q, Wang S J, Zheng L, Tian Q. Robust ImageGraph: Rank-level feature fusion for image search. IEEE Transactions on Image Processing, 2017, 26(7): 3128-3141 doi: 10.1109/TIP.2017.2660244
[52] Mafarja M, Aljarah I, Heidari A A, Hammouri A I, Faris H, Al-Zoubi A M, et al. Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowledge-Based Systems, 2018, 145: 25-45 doi: 10.1016/j.knosys.2017.12.037
[53] Faris H, Mafarja M M, Heidari A A, Aljarah I, Al-Zoubi M, Mirjalili S, et al. An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowledge-Based System, 2018, 154: 43-67 doi: 10.1016/j.knosys.2018.05.009
[54] Emary E, Zawbaa H M, Grosan C, Hassenian A E. Feature subset selection approach by gray-wolf optimization. In: Proceedings of the 1st International Afro-European Conference for Industrial Advancement. Cham, Germany: Springer, 2014. 1-13
[55] Cox D R. The regression analysis of binary sequences. Journal of the Royal Statistical Society: Series B (Methodological), 1958, 20(2): 215-232 doi: 10.1111/j.2517-6161.1958.tb00292.x
[56] Ho T K. Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition. Montreal, Canada: IEEE, 1995. 278-282 https://ieeexplore.ieee.org/document/598994
[57] Altman N S. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 1992, 46(3): 175-185 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1080/00031305.1992.10475879
[58] Quilan J R. Decision trees and multi-valued attributes. Machine Intelligence 11. New York, USA: Oxford University Press, 1988. 305-318
[59] Kononenko I. ID3, sequential Bayes, naive Bayes and Bayesian neural networks. In: Proceedings of European Working Session on Learning. 1989. 91-98
[60] Garreta R, Moncecchi G. Learning Scikit-Learn: Machine Learning in Python. Birmingham, England: Packt Publishing, 2013.
[61] Szegedy C, Ioffe S, Vanhoucke V, Alemi A A. Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence. San Francisco, California, USA: AAAI, 2017. 4278-4284
[62] Huang G, Liu Z, Van Der Maaten L, Weinberger K Q. Densely connected convolutional networks. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017. 2261-2269 https://ieeexplore.ieee.org/document/8099726
[63] Howard A G, Zhu M L, Chen B, Kalenichenko D, Wang W J, Weyand T, et al. MobileNets: Efficient convolutional neural networks for mobile vision applications[Online], available: https://arxiv.org/abs/1704.04861, April 17, 2017.
[64] Zhang H B, Qiu D D, Wu Rv Z, Deng Y X, Ji D H, Li T. Novel framework for image attribute annotation with gene selection XGBoost algorithm and relative attribute model. Applied Soft Computing, 2019, 80: 57-79 doi: 10.1016/j.asoc.2019.03.017
[65] Chen W H, Chen X T, Zhang J G, Huang K Q. Beyond triplet loss: A deep quadruplet network for person re-identification. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017. 1320 -1329
[66] Odena A, Olah C, Shlens J. Conditional image synthesis with auxiliary classifier GANs. In: Proceedings of the 34th International Conference on Machine Learning. Sydney, Australia, 2017.