Directed Markov Stationary Features for visual classification

We investigate how to effectively incorporate spatial structure information into histogram features for boosting visual classification performance motivated by recently proposed Markov Stationary Features (MSF). First, we show that due to the symmetric property of the image occurrence modeling procedure, the stationary distribution derived from the normalized co-occurrence matrix has a trivial informative solution which only approximates the original histogram representation, i.e., does not encode proper spatial structure information. To eliminate this ambiguity, we propose in this work the so called Directed Markov Stationary Features (DMSF) to encode spatial information into histogram features, and the asymmetric essence of the co-occurrence matrices in DMSF avoids the trivial informative solutions in MSF. Extensive experiments on face recognition show the significant performance improvement brought by our proposed DMSF.