Towards Understanding the Formation of Uniform Local Binary Patterns

Olli Lahdenoja,Jonne Poikonen,Mika Laiho

doi:10.1155/2013/429347

Abstract

The research reported in this paper focuses on the modeling of Local Binary Patterns (LBPs) and presents an a priori model where LBPs are considered as combinations of permutations. The aim is to increase the understanding of the mechanisms related to the formation of uniform LBPs. Uniform patterns are known to exhibit high discriminative capability; however, so far the reasons for this have not been fully explored. We report an observation that although the overall a priori probability of uniform LBPs is high, it is mostly due to the high probability of only certain classes of patterns, while the a priori probability of other patterns is very low. In order to examine this behavior, the relationship between the runs up and down test for randomness of permutations and the uniform LBPs was studied. Quantitative experiments were then carried out to show that the relative effect of uniform patterns to the LBP histogram is strengthened with deterministic data, in comparison with the i.i.d. model. This was verified by using an a priori model as well as through experiments with natural image data. It was further illustrated that specific uniform LBP codes can also provide responses to salient shapes, that is, to monotonically changing intensity functions and edges within the image microstructure.

Highlights

The Local Binary Pattern (LBP) methodology [1] was first proposed as a texture descriptor, but it has later been applied to various other fields of computer vision: for example, face recognition, facial expression recognition, modeling motion and actions, as well as medical image analysis
A considerable portion of this increase can be explained by the bilinear subsampling as described in [8], but we propose that a portion of this increase could be explained by the capability of the uniform patterns to respond to deterministic properties within the image microstructure
We proposed the modeling of LBPs through nonlinear intermediate mapping into permutations

Summary

Introduction

The Local Binary Pattern (LBP) methodology [1] was first proposed as a texture descriptor, but it has later been applied to various other fields of computer vision: for example, face recognition, facial expression recognition, modeling motion and actions, as well as medical image analysis. If a sample point is located between pixels, the intensity value used for the comparison can be determined by bilinear interpolation (see Figure 1). Using this sampling procedure, sweeping over the whole image is denoted by LBP(M, r) [9, 10]

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