  In this paper we proposed an ordered patch based method using Conditional
Random Field (CRF) in order to encode local properties and their spatial
relationship in images to address texture classification, face recognition, and
scene classification problems. Typical image classification approaches work
without considering spatial causality among distinctive properties of an image
for image representation in feature space. In this method first, each image is
encoded as a sequence of ordered patches, including local properties. Second,
the sequence of these ordered patches is modeled as a probabilistic feature
vector by CRF to model spatial relationship of these local properties. And
finally, image classification is performed on such probabilistic image
representation. Experimental results on several standard image datasets
indicate that proposed method outperforms some of existing image classification
methods.
