Machine Learning
Large-scale Learning with SVM and Convolutional for Generic Object Categorization
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
RVM-based multi-class classification of remotely sensed data
International Journal of Remote Sensing
Fuzzy segmentation for object-based image classification
International Journal of Remote Sensing
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Improving urban land cover classification using fuzzy image segmentation
Transactions on Computational Science VI
| Hi-index | 0.00 |
Support Vector Machines (SVM) is becoming a popular alternative to traditional image classification methods because it makes possible accurate classification from small training samples. Nevertheless, concerns regarding SVM parameterization and computational effort have arisen. This Letter is an evaluation of an automated SVM-based method for image classification. The method is applied to a land-cover classification experiment using a hyperspectral dataset. The results suggest that SVM can be parameterized to obtain accurate results while being computationally efficient. However, automation of parameter tuning does not solve all SVM problems. Interestingly, the method produces fuzzy image-regions whose contextual properties may be potentially useful for improving the image classification process.