Efficient Spatial Classification Using Decoupled Conditional Random Fields

We present a discriminative method to classify data that have interdependencies in 2-D lattice. Although both Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) are well-known methods for modeling such dependencies, they are often ineffective and inefficient, respectively. This is because many of the simplifying assumptions that underlie the MRF’s efficiency compromise its accuracy. As CRFs are discriminative, they are typically more accurate than the generative MRFs. This also means their learning process is more expensive. This paper addresses this situation by defining and using “Decoupled Conditional Random Fields (DCRFs)”, a variant of CRFs whose learning process is more efficient as it decouples the tasks of learning potentials. Although our model is only guaranteed to approximate a CRF, our empirical results on synthetic/real datasets show that DCRF is essentially as accurate as other CRF variants, but is many times faster to train.