The paper introduces a multiscale conditional random field (mCRF) for image labeling, which combines local, regional, and global features to improve the accuracy of pixel labeling. The mCRF model is designed to capture both local and global relationships in images, addressing the limitations of traditional Markov random fields (MRFs) and conditional random fields (CRFs). The model uses a statistical learning approach to learn features from labeled image data, incorporating a supervised version of the contrastive divergence algorithm for parameter estimation. The mCRF is evaluated on two real-world image databases, the Corel and Sowerby datasets, showing significant improvements over a classifier and an MRF in terms of classification accuracy and contextual labeling. The paper also discusses the advantages of the mCRF model over other methods, such as its ability to handle large-scale interactions and its efficient representation of label relationships.The paper introduces a multiscale conditional random field (mCRF) for image labeling, which combines local, regional, and global features to improve the accuracy of pixel labeling. The mCRF model is designed to capture both local and global relationships in images, addressing the limitations of traditional Markov random fields (MRFs) and conditional random fields (CRFs). The model uses a statistical learning approach to learn features from labeled image data, incorporating a supervised version of the contrastive divergence algorithm for parameter estimation. The mCRF is evaluated on two real-world image databases, the Corel and Sowerby datasets, showing significant improvements over a classifier and an MRF in terms of classification accuracy and contextual labeling. The paper also discusses the advantages of the mCRF model over other methods, such as its ability to handle large-scale interactions and its efficient representation of label relationships.