Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face Recognition Using Kernel Based Fisher Discriminant Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
A practical SVM-based algorithm for ordinal regression in image retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Random Subwindows for Robust Image Classification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Coupled Kernel-Based Subspace Learning
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Mercer Kernels for Object Recognition with Local Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Journal of Cognitive Neuroscience
Gabor feature based face recognition using kernel methods
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
AdaBoost gabor fisher classifier for face recognition
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Multiple similarities based kernel subspace learning for image classification
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
IEEE Transactions on Image Processing
Improving kernel Fisher discriminant analysis for face recognition
IEEE Transactions on Circuits and Systems for Video Technology
Similarity Features for Facial Event Analysis
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Multi-view kernel machine on single-view data
Neurocomputing
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In this paper, we first conceive a new perception of the kernel feature. The kernel subspace methods can be regarded as two independent steps: an explicit kernel feature extraction step and a linear subspace analysis step on the extracted kernel features. The kernel feature vector of an image is composed of dot products between the image and all the training images using nonlinear dot product kernel. Then, based on this perception, we further extend the kernel feature vector of an image to a kernel feature matrix for visual recognition. This extension takes different representation cues of images into account, respectively, while only global average information is used in the traditional kernel methods. From the view of dot product as similarity, this extension means using multiple similarities to measure two images, which is more accordant to human vision. In order to efficiently deal with the problem of numerical computation, a matrix-based kernel discriminant analysis algorithm is employed to learn discriminating kernel features for visual recognition. Experiments on the FERET face database, the COIL-100 object database, and the Wang's nature image database show the advantage of the proposed method.