We show that conditional random fields (CRFs) with learned heterogeneous graphs outperforms its pre-designated homogeneous counterparts with heuristics. Without introducing any additional annotations, we utilize four deep convolutional neural networks (CNNs) to learn the connections of one pixel to its left, top, upper-left, upper-right neighbors. The results are then fused to obtain the super-pixel-level CRF graphs. The model parameters of CRFs are learned via minimizing the negative pseudo-log-likelihood of the potential function. Our results show that the learned graph delivers significantly better segmentation results than CRFs with pre-designated graphs, and achieves state-of-the-art performance when combining with CNN features.
CITATION STYLE
Ding, F., Wang, Z., Guo, D., Chen, S., Zhang, J., & Shao, Z. (2018). Deep CRF-Graph learning for semantic image segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11013 LNAI, pp. 360–368). Springer Verlag. https://doi.org/10.1007/978-3-319-97310-4_41
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