@inproceedings {196, title = {Combining Sorted Random Features for Texture Classification}, booktitle = {International Conference on Image Processing}, year = {2011}, address = {Brussels}, abstract = {

This paper explores the combining of powerful local texture descriptors and the advantages over single descriptors for texture classification. The proposed system is composed of three components:
(i) highly discriminative and robust sorted random projections (SRP) features;
(ii) a global Bag-of-Words (BoW) model; and
(iii) the use of multiple kernel Support Vector Machines (SVMs) combining multiple features.

The proposed system is also very simple, stemming from
(1) the effortless extraction of the SRP features,
(2) the simple orderless histogramming in the BoW model,
(3) a strategy with low computational complexity for multiple kernel SVMs.

We have tested our texture classification system on three popular
and challenging texture databases and find that the SVMs combining of SRP features produces outstanding classification results, outperforming the state-of-the-art for CUReT (99.37\%) and KTH-TIPS (99.29\%), and with highly competitive results for UIUC (98.56\%).

}, keywords = {Compressed Sensing, kernel methods, Random projection, Rotation invariance, support vector machines, Texture classification}, author = {L Liu and P Fieguth and G Kuang} } @inproceedings {423, title = {Adaboost and support vector machines for white matter lesion segmentation in MR images}, booktitle = {27th Annual International Conference of the Engineering in Medicine and Biology Society}, year = {2005}, abstract = {

The use of two powerful classification techniques (boosting and SVM) is explored for the segmentation of white-matter lesions in the MRI scans of human brain. Simple features are generated from proton density (PD) scans. Radial basis function (RBF) based Adaboost technique and support vector machines (SVM) are employed for this task. The classifiers are trained on severe, moderate and mild cases. The segmentation is performed in T1 acquisition space rather than standard space (with more slices). Hence, the proposed approach requires less time for manual verification. The results indicate that the proposed approach can handle MR field inhomogeneities quite well and is completely independent from manual selection process so that it can be run under batch mode. Segmentation performance comparison with manual detection is also provided

}, keywords = {Adaboost, biomedical MRI, brain, human brain, image classification, image segmentation, medical image processing, MR images, proton density scans, radial basis function, radial basis function networks, support vector machines, T1 acquisition, white matter lesion segmentation}, doi = {http://dx.doi.org/10.1109/IEMBS.2005.1616447}, author = {A Quddus and P Fieguth and O Basir} }