Invariant Texture Recognition Using a Steerable Pyramid

A rotation-invariant texture recognition system is presented. A steerable oriented pyramid is used to extract representative features for the input textures. The steerability of the lter set allows a shift to a rotationally invariant representation via a DFT-encoding step. Supervised classiication follows. State-of-the-art recognition results are presented for a 30 texture database with a comparison across the performance of the K-nn, Back-Propagation and Rule-Based classiiers. In addition, high accuracy estimation of the input rotation angle is demonstrated. The extension of the system to scale-invariance is discussed. We assess the performance of scale-invariant classiication and we present a simple algorithm for doing so.

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