Median local ternary patterns optimized with rotation-invariant uniform-three mapping for noisy texture classification

Local ternary pattern (LTP) is a simple, yet high-discriminative texture feature model. In order to further promote the performance of LTP in noisy texture classification, this paper proposes a multi-scale median LTP (MLTP) framework. It firstly designs a non-overlapped median sampling scheme to resist against noise, and then re-defines central descriptor, radial descriptor and magnitude descriptor for MLTP. Moreover, it also proposes the pattern mapping solution named rotation-invariant uniform three (riu3) so as to project original MLTP codes from high-dimensional patterns to low-dimensional ones. In our multi-scale algorithm, every image is sampled and encoded separately by three MLTP descriptors, and then original patterns are mapped by a pre-stored riu3 pattern lookup table. Finally, the frequency histogram of joint distribution on three mapped MLTP code images is computed to generate the feature vector of given sampling scale. The different vectors under different scales are concatenated together to produce the objective vectors for training classifier or texture classification. The available classifiers for our algorithm include nearest neighbor classifier (NNC), and support vector machine (SVM), etc. The experiments on five publicly-available texture databases show that the multi-scale MLTP method proposed in this paper could obtain an average accuracy of 99.48% on dataset OTC12, 96.97% on UIUC, 98.71% on KTH-TIPS2b, 98.55% on Brodatz and 97.49% on ALOT. Compared with other state-of-the-art approaches, under the low-intensity conditions of Gaussian, Salt & Pepper and random pixel corruption noise, multi-scale MLTP + SVM could still keep higher accuracy and have better noise robustness than others.