Multi-scale Binary Patterns for Texture Analysis

Abstract

This paper presents two novel ways of extending the local binary pattern (LBP) texture analysis operator to multiple scales. First, large-scale texture patterns are detected by combining exponentially growing circular neighborhoods with Gaussian low-pass filtering. Second, cellular automata are proposed as a way of compactly encoding arbitrarily large circular neighborhoods. The performance of the extensions is evaluated in classifying natural textures from the Outex database.

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