Selecting the best performing rotation invariant patterns in local binary/ternary patterns


This paper purposes a new method for selecting the most discriminant rotation invariant patterns in local binary patterns and local ternary patterns. Our experiments show that a selection based on variance performs better than the recently proposed method of using dominant local binary patterns (DLBP). Our method uses a random subspace of patterns with higher variance. Features are transformed using Neighborhood Preserving Embedding (NPE) and then used to train a support vector machine. Moreover, we extend DLBP with local ternary patterns (DLTP) and examine methods for building a supervised random subspace of classifiers where each bin of the histogram has a probability of belonging to a given subspace according to its occurrence frequencies. We compare several texture descriptors and show that the random subspace ensemble based on NPE features outperforms other recent state-of-the-art approaches. This conclusion is based on extensive experiments conducted in several domains using five benchmark databases.


Information Technology and Cybersecurity

Document Type

Conference Proceeding


Local binary patterns, Local ternary patterns, Non-uniform patterns, Support vector machines, Texture descriptors

Publication Date


Journal Title

Proceedings of the 2010 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2010