Rotationally Invariant Hashing of Median Binary Patterns for Texture Classification

Authors:
Adel Hafiane;Guna Seetharaman;Kannappan Palaniappan;Bertrand Zavidovique
Affiliations:
Department of Computer Science, University of Missouri-Columbia, Columbia, USA 65211;Department of Electrical and Computer Engineering, Air Force Institute of Technology, Dayton, USA 45433-7765;Department of Computer Science, University of Missouri-Columbia, Columbia, USA 65211;Institut d'Electronique Fondamentale, Université de Paris-Sud, Orsay, France F-91405
Venue:
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Year:
2008

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Abstract

We present a novel image feature descriptor for rotationally invariant 2D texture classification. This extends our previous work on noise-resistant and intensity-shift invariant median binary patterns (MBPs), which use binary pattern vectors based on adaptive median thresholding. In this paper the MBPs are hashed to a binary chain or equivalence class using a circular bit-shift operator. One binary pattern vector (ie.smallest in value) from the group is selected to represent the equivalence class. The resolution and rotation invariant MBP (MBP ROT) texture descriptor is the distribution of these representative binary patterns in the image at one or more scales. A special subset of these rotation and scale invariant representative binary patterns termed uniformpatterns leads to a more compact and robust MBP descriptor (MBP UNIF) that outperforms the rotation invariant uniform local binary patterns (LBP UNIF). We quantitatively compare and demonstrate the advantage of the new MBP texture descriptors for classification using the Brodatz and Outex texture dictionaries.