1 research outputs found
Complex Relations in a Deep Structured Prediction Model for Fine Image Segmentation
Many deep learning architectures for semantic segmentation involve a Fully
Convolutional Neural Network (FCN) followed by a Conditional Random Field (CRF)
to carry out inference over an image. These models typically involve unary
potentials based on local appearance features computed by FCNs, and binary
potentials based on the displacement between pixels. We show that while current
methods succeed in segmenting whole objects, they perform poorly in situations
involving a large number of object parts. We therefore suggest incorporating
into the inference algorithm additional higher-order potentials inspired by the
way humans identify and localize parts. We incorporate two relations that were
shown to be useful to human object identification - containment and attachment
- into the energy term of the CRF and evaluate their performance on the Pascal
VOC Parts dataset. Our experimental results show that the segmentation of fine
parts is positively affected by the addition of these two relations, and that
the segmentation of fine parts can be further influenced by complex structural
features