Online updating regularized kernel

31-Jan-2016 05:39

Such segmentation is essential for studying anatomical structure changes and brain quantification [1].It is also a prerequisite for tumor growth modeling as tumors diffuse at different rates according to the surrounding tissues [2].A purely online model in this category would learn based on just the new input and some extra stored information (which is usually expected to have storage requirements independent of training data size).For many formulations, for example nonlinear kernel methods, true online learning is not possible, though a form of hybrid online learning with recursive algorithms can be used where .The algorithms have been validated against both synthetic and clinical magnetic resonance images with different types and levels of noises and compared with 6 recent soft clustering algorithms.Experimental results show that the proposed algorithms are superior in preserving image details and segmentation accuracy while maintaining a low computational complexity.IEEE Transactions on Pattern Analysis and Machine Intelligence 3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure Queirolo, C. [link] Non-Lambertian Reflectance Modeling and Shape Recovery for Faces Using Tensor Splines Kumar, R; Barmpoutis, A; Banerjee, A; Vemuri, B [link] Age-Invariant Face Recognition Unsang Park; Yiying Tong; Jain, A. [link] Decomposition of Complex Line Drawings with Hidden Lines for 3D Planar-Faced Manifold Object Reconstruction Liu, J; Chen, Y; Tang, X [link] 3D Face Recognition Using Iso-geodesic Stripes Berretti, S; Del Bimbo, A; Pala, P [link] Robust 3D Face Recognition by Local Shape Difference Boosting Wang, Y; Liu, J; Tang, X [link] A Unified Probabilistic Framework for Spontaneous Facial Action Modeling and Understanding Yan Tong; Jixu Chen; Qiang Ji [link] A Dynamic Texture Based Approach to Recognition of Facial Actions and their Temporal Models Koelstra, S; Pantic, M; Patras, I [link] IEEE Transactions on Information Forensics and Security Face Matching and Retrieval Using Soft Biometrics Park, U.; Jain, A. [link] A Hybrid Approach for Generating Secure and Discriminating Face Template Feng, Y. [link] Extracting Multiple Features in the CID Color Space for Face Recognition Liu, Z.; Yang, J.; Liu, C. [link] Regularized Locality Preserving Projections and Its Extensions for Face Recognition Jiwen Lu; Yap-Peng Tan [link] A Component-Based Framework for Generalized Face Alignment Huang, Y.; Liu, Q.; Metaxas, D. [link] Graph-Preserving Sparse Nonnegative Matrix Factorization With Application to Facial Expression Recognition Zhi, R.; Flierl, M.; Ruan, Q.; Kleijn, W. [link] IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications Incremental Embedding and Learning in the Local Discriminant Subspace With Application to Face Recognition Cheng, M.; Fang, B.; Tang, Y. [link] Combining Perceptual Features With Diffusion Distance for Face Recognition Zhou, H.; Sadka, A. [link] International Journal of Computer Vision Estimating Facial Reflectance Properties Using Shape-from-Shading William A. Yuen [link] Face recognition using discriminant locality preserving projections based on maximum margin criterion Gui-Fu Lu, Zhong Lin, Zhong Jin [link] Face recognition using Intrinsicfaces Yong Wang, Yi Wu [link] Context-aware fusion: A case study on fusion of gait and face for human identification in video Xin Geng, Kate Smith-Miles, Liang Wang, Ming Li, Qiang Wu [link] SVM-based feature extraction for face recognition Sang-Ki Kim, Youn Jung Park, Kar-Ann Toh, Sangyoun Lee [link] Fusion of color, local spatial and global frequency information for face recognition Zhiming Liu, Chengjun Liu [link] Incremental template updating for face recognition in home environments Annalisa Franco, Dario Maio, Davide Maltoni [link] Infrared gait recognition based on wavelet transform and support vector machine Zhaojun Xue, Dong Ming, Wei Song, Baikun Wan, Shijiu Jin [link] Super-resolution of human face image using canonical correlation analysis Hua Huang, Huiting He, Xin Fan, Junping Zhang [link] A performance driven methodology for cancelable face templates generation Youngsung Kim, Andrew Beng Jin Teoh, Kar-Ann Toh [link] Emulating biological strategies for uncontrolled face recognition Weihong Deng, Jiani Hu, Jun Guo, Weidong Cai, Dagan Feng [link] Hallucinating face by position-patch Xiang Ma, Junping Zhang, Chun Qi [link] Interesting faces: A graph-based approach for finding people in news Derya Ozkan, Pinar Duygulu [link] Local contrast enhancement and adaptive feature extraction for illumination-invariant face recognition Wen-Chung Kao, Ming-Chai Hsu, Yueh-Yiing Yang [link] Robust, accurate and efficient face recognition from a single training image: A uniform pursuit approach Weihong Deng, Jiani Hu, Jun Guo, Weidong Cai, Dagan Feng [link] Real-time 2D 3D facial action and expression recognition Filareti Tsalakanidou, Sotiris Malassiotis [link] A novel Bayesian logistic discriminant model: An application to face recognition R. Boufama, Djemel Ziou, Bernard Colin [link] Feature extraction using discrete cosine transform and discrimination power analysis with a face recognition technology Saeed Dabbaghchian, Masoumeh P. Frossard [link] Projective active shape models for pose-variant image analysis of quasi-planar objects: Application to facial analysis Federico M.

online updating regularized kernel-76

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This problem is tackled by incremental learning approaches.

Discriminant analysis is also different from factor analysis in that it is not an interdependence technique: a distinction between independent variables and dependent variables (also called criterion variables) must be made.

LDA works when the measurements made on independent variables for each observation are continuous quantities.

is a space of functions called a hypothesis space, so that some notion of total loss is minimised.

online updating regularized kernel-40

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Depending on the type of model (statistical or adversarial), one can devise different notions of loss, which lead to different learning algorithms.

[link] Face Recognition by Exploring Information Jointly in Space, Scale and Orientation Lei, Z.; Liao, S.; Pietikainen, M.; Li, S. Ghaemmaghami, Ali Aghagolzadeh [link] Color space normalization: Enhancing the discriminating power of color spaces for face recognition Jian Yang, Chengjun Liu, Lei Zhang [link] 3D object classification using salient point patterns with application to craniofacial research Indriyati Atmosukarto, Katarzyna Wilamowska, Carrie Heike, Linda G.