Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Quasiconformal Kernel Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative Kernel Principal Component Analysis for Image Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Matched Subspace Detectors for Hyperspectral Target Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subclass Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
MultiK-MHKS: A Novel Multiple Kernel Learning Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-class Discriminant Kernel Learning via Convex Programming
The Journal of Machine Learning Research
Mercer’s theorem, feature maps, and smoothing
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Kernel-based feature extraction with a speech technology application
IEEE Transactions on Signal Processing
A criterion for optimizing kernel parameters in KBDA for image retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Image classification using spectral and spatial information based on MRF models
IEEE Transactions on Image Processing
Nonlinear kernel-based statistical pattern analysis
IEEE Transactions on Neural Networks
Foley-Sammon optimal discriminant vectors using kernel approach
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Nonlinear kernel-based feature extraction algorithms have recently been proposed to alleviate the loss of class discrimination after feature extraction. When considering image classification, a kernel function may not be sufficiently effective if it depends only on an information resource from the Euclidean distance in the original feature space. This study presents an extended radial basis kernel function that integrates multiple discriminative information resources, including the Euclidean distance, spatial context, and class membership. The concepts related to Markov random fields (MRFs) are exploited to model the spatial context information existing in the image. Mutual closeness in class membership is defined as a similarity measure with respect to classification. Any dissimilarity from the additional information resources will improve the discrimination between two samples that are only a short Euclidean distance apart in the feature space. The proposed kernel function is used for feature extraction through linear discriminant analysis (LDA) and principal component analysis (PCA). Experiments with synthetic and natural images show the effectiveness of the proposed kernel function with application to image classification.