提出了一种新颖的高阶CRF模型，能够同时获得语义分割和目标检测结果。该高阶CRF模型由低阶能量项和改进目标检测能量项构成。该模型采用了一二阶合并方法和逻辑斯蒂回归，从而降低了由于初始检测不准确而导致的错误识别率。在MSRC 21和PASCAL VOC 2007两组数据库上进行的实验表明，该方法显著优于传统方法。%Computer vision algorithms for individual tasks such as object recognition, detection and segmentation have shown impressive results in the recent years. The next challenge is to integrate all these algorithms and address the problem of scene understanding. A new higher order conditional random field (CRF) model is proposed to get semantic segmentation and object detection simultaneously. Specifically, the proposed higher order CRF model consists of low-order potentials and improved detector potentials. To avoid wrong recognition caused by the confidence given by the initial detector, the first-and-second-order pooling and logistic regression are adopted to improve the detector potential. Experimental results show that the proposed model achieves significant improvement over the baseline methods on MSRC 21-class and PASCAL VOC 2007 datasets.
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