Object Boundary Guided Semantic Segmentation
This work improves semantic segmentation accuracy for computer vision applications, but it is incremental as it builds on existing FCN methods by incorporating boundary guidance.
The paper tackles the problem of semantic segmentation by addressing the lack of object boundary information in fully-convolutional neural networks, resulting in improved segmentation quality on the PASCAL VOC benchmark.
Semantic segmentation is critical to image content understanding and object localization. Recent development in fully-convolutional neural network (FCN) has enabled accurate pixel-level labeling. One issue in previous works is that the FCN based method does not exploit the object boundary information to delineate segmentation details since the object boundary label is ignored in the network training. To tackle this problem, we introduce a double branch fully convolutional neural network, which separates the learning of the desirable semantic class labeling with mask-level object proposals guided by relabeled boundaries. This network, called object boundary guided FCN (OBG-FCN), is able to integrate the distinct properties of object shape and class features elegantly in a fully convolutional way with a designed masking architecture. We conduct experiments on the PASCAL VOC segmentation benchmark, and show that the end-to-end trainable OBG-FCN system offers great improvement in optimizing the target semantic segmentation quality.