Kernel-mapped histograms of multi-scale LBPs for tree bark recognition

We propose a novel method for tree bark identification by SVM classification of feature-mapped multi-scale descriptors formed by concatenated histograms of Local Binary Patterns (LBPs). A feature map approximating the histogram intersection kernel significantly improves the methods accuracy. Contrary to common practice, we use the full 256 bin LBP histogram rather than the standard 59 bin histogram of uniform LBPs and obtain superior results. Robustness to scale changes is handled by forming multiple multi-scale descriptors. Experiments conducted on a standard dataset show 96.5% accuracy using ten-fold cross validation. Using the standard 15 training examples per class, the proposed method achieves a recognition rate of 82.5% and significantly outperforms both the state-of-the-art automatic recognition rate of 64.2% and human experts with recognition rates of 56.6% and 77.8%. Experiments on standard texture datasets confirm that the proposed method is suitable for general texture recognition.