Blur Insensitive Texture Classification Using Local Phase Quantization

Authors:
Ville Ojansivu;Janne Heikkilä
Affiliations:
Machine Vision Group, Department of Electrical and Information Engineering, University of Oulu, PO Box 4500, Finland 90014;Machine Vision Group, Department of Electrical and Information Engineering, University of Oulu, PO Box 4500, Finland 90014
Venue:
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Year:
2008

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Abstract

In this paper, we propose a new descriptor for texture classification that is robust to image blurring. The descriptor utilizes phase information computed locally in a window for every image position. The phases of the four low-frequency coefficients are decorrelated and uniformly quantized in an eight-dimensional space. A histogram of the resulting code words is created and used as a feature in texture classification. Ideally, the low-frequency phase components are shown to be invariant to centrally symmetric blur. Although this ideal invariance is not completely achieved due to the finite window size, the method is still highly insensitive to blur. Because only phase information is used, the method is also invariant to uniform illumination changes. According to our experiments, the classification accuracy of blurred texture images is much higher with the new method than with the well-known LBP or Gabor filter bank methods. Interestingly, it is also slightly better for textures that are not blurred.