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CRF Learning with CNN Features for Image Segmentation
Conditional Random Rields (CRF) have been widely applied in image
segmentations. While most studies rely on hand-crafted features, we here
propose to exploit a pre-trained large convolutional neural network (CNN) to
generate deep features for CRF learning. The deep CNN is trained on the
ImageNet dataset and transferred to image segmentations here for constructing
potentials of superpixels. Then the CRF parameters are learnt using a
structured support vector machine (SSVM). To fully exploit context information
in inference, we construct spatially related co-occurrence pairwise potentials
and incorporate them into the energy function. This prefers labelling of object
pairs that frequently co-occur in a certain spatial layout and at the same time
avoids implausible labellings during the inference. Extensive experiments on
binary and multi-class segmentation benchmarks demonstrate the promise of the
proposed method. We thus provide new baselines for the segmentation performance
on the Weizmann horse, Graz-02, MSRC-21, Stanford Background and PASCAL VOC
2011 datasets
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