Active shape models—their training and application
Computer Vision and Image Understanding
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Feature Extraction and Classification
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Twin Kernel Embedding with Back Constraints
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Describing Visual Scenes Using Transformed Objects and Parts
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning from Multiple Sources
The Journal of Machine Learning Research
Guest Editorial: Learning from multiple sources
Machine Learning
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Kernel laplacian eigenmaps for visualization of non-vectorial data
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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In this paper, we propose a tensor kernel on images which are described as set of local features and then apply a novel dimensionality reduction algorithm called Twin Kernel Embedding (TKE) [1] on it for images manifold learning. The local features of the images extracted by some feature extraction methods like SIFT [2] are represented as tuples in the form of coordinates and feature descriptor which are regarded as highly structured data. In [3], different kernels were used for intra and inter images similarity. This is problematic because different kernels refer to different feature spaces and hence they are representing different measures. This heterogeneity embedded in the kernel Gram matrix which was input to a dimensionality reduction algorithm has been transformed to the image embedding space and therefore led to unclear understanding of the embedding. We address this problem by introducing a tensor kernel which treats different sources of information in a uniform kernel framework. The kernel Gram matrix generated by tensor kernel is homogeneous, that is all elements are from the same measurement. Image manifold learned from this kernel is more meaningful. Experiments on image visualization are used to show the effectiveness of this method.