@article {122, title = {Sorted Random Projections for Robust Rotation Invariant Texture Classification}, journal = {Pattern Recognition}, volume = {45}, year = {2012}, month = {05/12}, pages = {2405-2418}, abstract = {

This paper presents a simple, novel, yet very powerful approach for robust rotation-invariant texture classification based on random projection. The proposed sorted random projection maintains the strengths of random projection, in being computationally efficient and low-dimensional, with the addition of a straightforward sorting step to introduce rotation invariance. At the feature extraction stage, a small set of random measurements is extracted from sorted pixels or sorted pixel differences in local image patches. The rotation invariant random features are embedded into a bag-of-words model to perform texture classification, allowing us to achieve global rotation invariance. The proposed unconventional and novel random features are very robust, yet by leveraging the sparse nature of texture images, our approach outperforms traditional feature extraction methods which involve careful design and complex steps. We report extensive experiments comparing the proposed method to six state-of-the-art methods, RP, Patch, LBP, WMFS and the methods of Lazebnik et al. and Zhang et al., in texture classification on five databases: CUReT, Brodatz, UIUC, UMD and KTH-TIPS. Our approach leads to significant improvements in classification accuracy, producing consistently good results on each database, including what we believe to be the best reported results for Brodatz, UMD and KTH-TIPS

}, keywords = {Bag of Words, Compressed Sensing, Feature Extraction, Image patches, Random projection, Rotation invariance, Texture classification}, author = {L Liu and P Fieguth and D A. Clausi and G Kuang} } @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} }