Combining Sorted Random Features for Texture Classification

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%).