Image Classification Based on Conditional Random Fields

We use Conditional Random Fields (CRFs) to classify regions in an image. CRFs provide a discriminative framework to incorporate spatial dependencies in an image, which is more appropriate for classification tasks as opposed to a generative framework. In this paper we apply CRFs to the image multi-classification task, we focus on three aspects of the classification task: feature extraction, the Original feature clustering based on K-means, and feature vector modeling base on CRF to obtain multiclass classification. We present classification results on sample images from Cambridge (MSRC) database, and the experimental results show that the method we present can classify the images accurately.