@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} } @article {138, title = {Texture classification from random features}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {34}, year = {2012}, pages = {574 - 586}, abstract = {

This paper presents a simple, novel, yet very powerful approach for texture classification based on compressed sensing, suitable for large texture database applications. At the feature extraction stage, a small set of random features is extracted from local image patches. The random features are embedded into a bag of words model to perform texture classification, thus learning and classification are carried out in the compressed sensing domain. The proposed unconventional random feature extraction is simple, yet by leveraging the sparse nature of texture images, our approach outperforms traditional feature extraction methods which involve careful design and complex steps. We have conducted extensive experiments on both the CUReT and the Brodatz databases, comparing the proposed approach to three state-of-the-art texture classification methods: Patch, MR8, and LBP. We show that our approach leads to significant improvements in classification accuracy and reductions in feature dimensionality.

}, keywords = {Bag of Words, Compressed Sensing, Image patches, textons, Texture classification}, author = {L Liu and P Fieguth} } @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 {263, title = {Texture classification using compressed sensing}, booktitle = {This paper presents a simple, novel, yet very powerful approach for texture classification based on compressed sensing and bag of words model, suitable for large texture database applications with images obtained under unknown viewpoint and illumination. }, year = {2010}, abstract = {

This paper presents a simple, novel, yet very powerful approach for texture classification based on compressed sensing and bag of words model, suitable for large texture database applications with images obtained under unknown viewpoint and illumination. At the feature extraction stage, a small set of random features are extracted from local image patches. The random features are embedded into the bag of words model to perform texture classification. Random feature extraction surpasses many conventional feature extraction methods, despite their careful design and complexity. We conduct extensive experiments on the CUReT database to evaluate the performance of the proposed approach. It is demonstrated that excellent performance can be achieved by the proposed approach using a small number of random features, as long as the dimension of the feature space is above certain threshold. Our approach is compared with recent state-of-the-art methods: the Patch method (Varma and Zisserman, TPAMI 09), the MR8 filter bank method (Varma and Zisserman, IJCV 05) and the LBP method (Ojala et al., TPAMI 02). It is shown that the proposed method significantly outperforms MR8 and LBP and is at least as good as the Patch method with drastic reduction in storage and computational complexity.

}, keywords = {bag of words model, band-pass filters, Compressed Sensing, computational complexity, Feature Extraction, image classification, image texture, LBP method, MR8 filter bank method, patch method, Texture classification, texture database applications}, doi = {http://dx.doi.org/10.1109/CRV.2010.16}, author = {L Liu and P Fieguth} } @inproceedings {366, title = {A medical texture local binary pattern for TRUS prostate segmentation}, booktitle = {29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2007}, abstract = {

Prostate cancer diagnosis and treatment rely on segmentation of Transrectal Ultrasound (TRUS) prostate images. This is a challenging and difficult task dut to weak prostate boundaries, speckle noise and the short range of gray levels. Advances in digital imaging techniques have made it possible the acquisition of large volumes of TRUS prostate images so that there is considerable demand for automated segmentation systems. Local binary pattern (LBP) has been used for texture segmentation and analysis. Despite its promising performance for texture classification it has not yet been considered for TRUS prostate segmentation. In this paper we introduce a medical texture local binary pattern operator designed for applications of medical imaging where different tissues or micro organisms might maintain extremely weak underlying textures that make it impossible or very difficult for ordinary texture analysis approaches to classify them. In the proposed method the deformations of a level set contour are controlled based on the medical texture local binary pattern operator.

}, keywords = {automated segmentation systems, biomedical ultrasonics, cancer, digital imaging techniques, image classification, image segmentation, image texture, LBP, level set contour deformations, medical image processing, medical texture local binary pattern, prostate cancer diagnosis, prostate cancer treatment, short gray level range, speckle noise, texture analysis, Texture classification, texture segmentation, transrectal ultrasound prostate image segmentation, TRUS prostate segmentation}, doi = {http://dx.doi.org/10.1109/IEMBS.2007.4353617}, author = {N Nezamoddin Kachouie and P Fieguth} }