A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Machine Learning
A Two-Stage Level Set Evolution Scheme for Man-Made Objects Detection in Aerial Images
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Kernel matching pursuit classifier ensemble
Pattern Recognition
Kernel matching pursuit for large datasets
Pattern Recognition
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
IEEE Transactions on Image Processing
Wavelet-based level set evolution for classification of textured images
IEEE Transactions on Image Processing
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This paper describes a new aerial images segmentation algorithm. Kernel Matching Pursuit (KMP) method is introduced to deal with the nonlinear distribution of the man-made objects' features in the aerial images. In KMP algorithm, a lot of training samples containing substantive information are used to detect the man-made objects. With KMP classifier, pixels in large aerial images will be labeled as different prediction values, which can be classified linearly. Then the modified Mumford-Shah model, which comprises the features of the KMP prediction values, is built to segment the aerial image by necessary level set evolution. The proposed method is proven to be effective by the results of experiments.