Making large-scale support vector machine learning practical
Advances in kernel methods
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
In Situ Adaptive Feature Extraction for Underwater Target Classification
AIPR '07 Proceedings of the 36th Applied Imagery Pattern Recognition Workshop
Kernel-matching pursuits with arbitrary loss functions
IEEE Transactions on Neural Networks
Multiresolution direction filterbanks: theory, design, and applications
IEEE Transactions on Signal Processing - Part I
IEEE Transactions on Signal Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
This research presents a contourlet based detection and feature extraction method for underwater targets. The method operates on Side Scan Sonar (SSS) images and is designed to automatically detect and generate target features for classification. Kernel based classifiers are used to determine the best boundary for separating targets and clutter. A statistically significant target data set is generated by embedding additional synthetic targets into SSS data collected during sea tests. Feature trade off studies show an improvement in classification results with the addition of directional based features.