Structured Ordinal Features for Appearance-Based Object Representation

In this paper, we propose a novel appearance-based representation, called Structured Ordinal Feature (SOF). SOF is a binary string encoded by combining eight ordinal blocks in a circle symmetrically. SOF is invariant to linear transformations on images and is flexible enough to represent different local structures of different complexity. We further extend SOF to Multi-scale Structured Ordinal Feature (MSOF) by concatenating binary strings of multi-scale SOFs at a fix position. In this way, MSOF encodes not only microstructure but also macrostructure of image patterns, thus provides a more powerful image representation. We also present an efficient algorithm for computing MSOF using integral images. Based on MSOF, statistical analysis and learning are performed to select most effective features and construct classifiers. The proposed method is evaluated with face recognition experiments, in which we achieve a high rank-1 recognition rate of 98.24% on FERET database.