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
Local binary patterns, Local ternary patterns, Non-uniform patterns, Support vector machines, Texture descriptors
Lumini, Loris Nanni1 Sheryl Brahnam2 Alessandra. "Selecting the best performing rotation invariant patterns in local binary/ternary patterns." In International conference on IP, computer vision, and pattern recognition. 2010.
Proceedings of the 2010 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2010