Machine Learning
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prior knowledge in support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Fast SVM Training Algorithm with Decomposition on Very Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
A novel SVM Geometric Algorithm based on Reduced Convex Hulls
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
Level set analysis for leukocyte detection and tracking
IEEE Transactions on Image Processing
Image segmentation and selective smoothing by using Mumford-Shah model
IEEE Transactions on Image Processing
Segmentation of thin structures in volumetric medical images
IEEE Transactions on Image Processing
A binary level set model and some applications to Mumford-Shah image segmentation
IEEE Transactions on Image Processing
Face recognition using recursive Fisher linear discriminant
IEEE Transactions on Image Processing
Unsupervised multiscale color image segmentation based on MDL principle
IEEE Transactions on Image Processing
Level Set Segmentation With Multiple Regions
IEEE Transactions on Image Processing
A fast iterative nearest point algorithm for support vector machine classifier design
IEEE Transactions on Neural Networks
A geometric approach to Support Vector Machine (SVM) classification
IEEE Transactions on Neural Networks
Leukocyte image segmentation using simulated visual attention
Expert Systems with Applications: An International Journal
LS-SVM based image segmentation using color and texture information
Journal of Visual Communication and Image Representation
Color texture segmentation based on image pixel classification
Engineering Applications of Artificial Intelligence
Scale selection for supervised image segmentation
Image and Vision Computing
Information Sciences: an International Journal
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Recently, researchers are focusing more on the study of support vector machine (SVM) due to its useful applications in a number of areas, such as pattern recognition, multimedia, image processing and bioinformatics. One of the main research issues is how to improve the efficiency of the original SVM model, while preventing any deterioration of the classification performance of the model. In this paper, we propose a modified SVM based on the properties of support vectors and a pruning strategy to preserve support vectors, while eliminating redundant training vectors at the same time. The experiments on real images show that (1) our proposed approach can reduce the number of input training vectors, while preserving the support vectors, which leads to a significant reduction in the computational cost while attaining similar levels of accuracy. (2)The approach also works well when applied to image segmentation.