Rotation-Invariant texture classification using steerable gabor filter bank

Authors:
Wumo Pan;T. D. Bui;C. Y. Suen
Affiliations:
Dept. Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada;Dept. Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada;Centre for Pattern Recognition and Machine Intelligence, Concordia University, Montréal, Québec, Canada
Venue:
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Year:
2005

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Abstract

An efficient rotation invariant feature extraction technique for texture classification based on Gabor multi-channel filtering is proposed. In this technique, Gabor function is approximated by a set of steerable basis functions, which results in a significant saving in the computation cost. The classification of 15 classes of Brodatz textures are considered in our experiments. Results show that up to 40% of computation can be saved compared with traditional Gabor multi-channel filtering method. In the mean time, almost the same high texture classification correct rate can be achieved.