Abstract & Overview
Texture analysis plays an important role in computer vision and pattern recognition applications. During the last few decades, the research community has proposed a large number of techniques for describing, retrieving and classifying texture images. Local Binary Patterns (LBP) coding is a state-of-the-art technique characterized by its simplicity and efficiency. Due to its success, several LBP-variants are proposed in recent literature. In this paper we show that the performance of LBP-based methods can be further improved by introducing a simple modification to the feature extraction process. We suggest building two different LBP histograms one for edge pixels and the second for non-edge pixels. The final feature vector is a weighted combination of the two histograms. This idea is mainly inspired by the results of several research works on vision indicating that when looking at objects, human attention focuses more on salient regions (where changes in intensity, color, etc. occur). The experiments that have been conducted on Brodatz and Outex databases show that implementing this modification on LBP-based techniques (LBP, LTP and LBP_V), produces significant improvement in the accuracy of the original methods.