为了实现复杂自然场景中多类目标的识别与分割，利用条件随机场(CRF)对目标特征进行建模，并在此基础上运用过分割算法将图片分为有限个连续区域，提出一种新的基于区域的CRF模型，即R-CRF模型，并采用Joint-boost算法对标注样本进行训练，研究基于主题的R-CRF模型在多类目标识别与分割中的应用。MSRC-21类数据库的实验结果表明，该算法在多类目标识别与分割中取得的结果优于国内外其他算法，尤其对于其他算法中正确率很低的形状多变而样本少的高结构物体的识别和分割取得了很好的结果。

A conditional random field (CRF) model is used to incorporate different feature potentials of objects for multi-class object recognition and segmentation in natural images.By using an over-segmentation algorithm，we propose a new region based CRF model called R-CRF model.We train our model on annotated samples by using Joint-boost algorithm and investigate the performance of the theme based R-CRF model for class based pixel-wise segmentation of images.We compare our results with recent published results on the MSRC 21-class database.The result shows that our theme based R-CRF model significantly outperforms the current state-of-the-art.Especially，by introducing theme and regions，our model obtains greatly improved accuracy of structured classes with high visual variability and fewer training examples.