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.

A Study for Selecting the Best Performing Rotation Invariant Patterns in Local Binary/Ternary Patterns

NANNI, LORIS;LUMINI, ALESSANDRA
2010

Abstract

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.
proceedings International Conference on Image Processing, Computer Vision & Pattern Recognition (IPCV'10)
369
375
L. Nanni; S. Brahnam; A. Lumini
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/96796
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