Abstract：The drawing style recognition of facial sketches is widely used for painting authentication and criminal investigation. A drawing style recognition algorithm of facial sketch based on multiple kernel learning is presented. Firstly, according to the way of art critics recognize the drawing style of facial sketch, five parts, the face part, left eye part, right eye part, nose part and mouth part, are extracted from the facial sketch. Then, gray histogram feature, gray moment feature, speeded-up robust feature and multiscale local binary pattern feature are extracted from each part on the basis of artists′ different understandings of lights and shadows on a face and various usages of the pencil . Finally, different parts and features are integrated and the drawing styles of facial sketches are classified by multiple kernel learning. Experimental results demonstrate that the proposed algorithm has better performance and obtains higher recognition rates.
张铭津，李洁，王楠楠. 基于多核学习的画像画风的识别*[J]. 模式识别与人工智能, 2015, 28(9): 822-827.
ZHANG Ming-Jin , LI Jie , WANG Nan-Nan. Drawing Style Recognition of Facial Sketch Based on Multiple Kernel Learning. , 2015, 28(9): 822-827.
[1] Friedman J, Hastie T, Tibshirani R. Additive Logistic Regression: A Statistical View of Boosting. The Annals of Statistics, 2000, 28(2): 337-407 [2] Vapnik V N. The Nature of Statistical Learning Theory. New York, USA: Springer-Verlag, 1999 [3] Cutzu F, Hammoud R, Leykin A. Estimating the Photorealism of Images: Distinguishing Paintings from Photograph // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Madison, USA, 2003, II: 305-312 [4] Li J, Wang J Z. Studying Digital Imagery of Ancient Paintings by Mixtures of Stochastic Models. IEEE Trans on Image Processing, 2004, 13(3): 340-353 [5] Zhang M J, Li J, Wang N N, et al. Recognition of Facial Sketch Styles. Neurocomputing: Part C, 2015, 149: 1188-1197 [6] Edwards B. The New Drawing on the Right Side of the Brain. New York, USA: Tarcher, 2009 [7] Dodson B. Keys to Drawing. Blue Hill, USA: North Light Book, 1990 [8] Bay H, Tuytelaars T, Gool L V. SURF: Speeded Up Robust Features // Proc of the 9th European Conference on Computer Vision. Graz, Austria, 2006, I: 404-417 [9] Kennedy J, Eberhart R. Particle Swarm Optimization // Proc of the IEEE International Conference on Neural Networks. Perth, Austra-lia, 1995, IV: 1942-1948 [10] Klare B F, Li Z F, Jain A K. Matching Forensic Sketches to Mug Shot Photos. IEEE Trans on Pattern Analysis and Machine Intelligence, 2011, 33(3): 639-646 [11] Pavlidis P, Weston J, Cai J S, et al. Gene Functional Classification from Heterogeneous Data // Proc of the 5th Annual International Conference on Computational Biology. Montreal, Canada, 2001: 249-255 [12] Bennett K P, Momma M, Embrechts M J. MARK: A Boosting Algorithm for Heterogeneous Kernel Models // Proc of the 8th ACM-SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Canada, 2002: 24-31 [13] Ben-Hur A, Noble W S. Kernel Methods for Predicting Protein-Protein Interactions. Bioinformatics, 2005, 21(1): 38-46 [14] Lewis D P, Jebara T, Noble W S. Nonstationary Kernel Combination // Proc of the 23rd International Conference on Machine Learning. Pittsburgh, USA, 2006: 553-560 [15] Sonnenburg S, Rtsch G, Schfer C, et al. Large Scale Multiple Kernel Learning. Journal of Machine Learning Research, 2006, 7: 1531-1565 [16] Bach F R. Consistency of the Group Lasso and Multiple Kernel Learning. Journal of Machine Learning Research, 2008, 9: 1179-1225 [17] Ong C S, Smola A J, Williamson R C. Learning the Kernel with Hyperkernels. Journal of Machine Learning Research, 2005, 6: 1043-1071 [18] Kingsbury N, Tay D B H, Palaniswami M. Multi-scale Kernel Methods for Classification // Proc of the IEEE Workshop on Machine Learning for Signal Processing. Mystic, USA, 2005: 43-48 [19] Zheng D N, Wang J X, Zhao Y N. Non-flat Function Estimation with a Multi-scale Support Vector Regression. Neurocomputing, 2006, 70(1/2/3): 420-429 [20] Yang Z, Guo J, Xu W R, et al. Multi-scale Support Vector Machine for Regression Estimation // Proc of the 3rd International Symposium on Neural Networks. Chengdu, China, 2006: 1030-1037 [21] Lanckriet G R G, Cristianini N, Bartlett P, et al. Learning the Kernel Matrix with Semidefinite Programming. Journal of Machine Learning Research, 2004, 5: 27-72 [22] Lee W J, Verzakov S, Duin R P W. Kernel Combination versus Classifier Combination // Proc of the 7th International Workshop on Multiple Classifier Systems. Prague, Czech Republic, 2007: 22-31 [23] Rakotomamonjy A, Bach F R, Canu S, et al. More Efficiency in Multiple Kernel Learning [EB/OL].[2014-08-20].http://www.di.ens.fr/~fbach/mkl_descent.pdf [24] Rakotomamonjy A, Bach F R, Canu S, et al. Simple MKL. Journal of Machine Learning Research, 2008, 9: 2491-2521 [25] Bucak S S, Jin R, Jain A K. Multiple Kernel Learning for Visual Object Recognition: A Review. IEEE Trans on Pattern Analysis and Machine Intelligence, 2014, 36(7): 1354-1369 [26] Xia H, Hoi S C H, Jin R, et al. Online Multiple Kernel Similarity Learning for Visual Search. IEEE Trans on Pattern Analysis and Machine Intelligence, 2014, 36(3): 536-549 [27] Li C, Georgiopoulos M, Anagnostopoulos G C. A Unifying Framework for Typical Multitask Multiple Kernel Learning Problems. IEEE Trans on Neural Networks and Learning Systems, 2014, 25(7): 1287-1297 [28] Gao X B, Gao F, Tao D C, et al. Universal Blind Image Quality Assessment Metrics via Natural Scene Statistics and Multiple Kernel Learning. IEEE Trans on Neural Networks and Learning Systems, 2013, 24(12): 2013-2026
2015, Vol. 28 
