A SCALE-ADAPTIVE EXTENSION TO METHODS BASED ON LBP USING SCALE-NORMALIZED LAPLACIAN OF GAUSSIAN EXTREMA IN SCALE-SPACE

摘要

Local Binary Patterns and its derivatives have been widely used in the field of texture recognition over the last decade. A restriction of methods based on LBP is the variance in terms of signal scaling. This is mainly caused by the fixed LBP radius and the fixed support area of sampling points. In this work we present a general framework to enhance the scale-invariance of all LBP flavored methods, which can be applied to existing methods with minimal effort. Based on scale-normalized Laplacian of Gaussian extrema in scale-space, the global scale of a texture in question is estimated, combined with a confidence measure, to compute scale adapted patterns. By using the notion of intrinsic scales, textures are analyzed at appropriate LBP scales. A comprehensive experimental study shows that the scale-invariance of three different LBP based methods (LBP, LTP, Fuzzy LBP) is highly improved by the proposed extension.