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19 April 2024 |
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Article overview
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Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform | | Liang-Chieh Chen
; Jonathan T. Barron
; George Papandreou
; Kevin Murphy
; Alan L. Yuille
; | | Date: |
10 Nov 2015 | | Abstract: | Deep convolutional neural networks (CNNs) are the backbone of state-of-art
semantic image segmentation systems. Recent work has shown that complementing
CNNs with fully-connected conditional random fields (CRFs) can significantly
enhance their object localization accuracy, yet dense CRF inference is
computationally expensive. We propose replacing the fully-connected CRF with
domain transform (DT), a modern edge-preserving filtering method in which the
amount of smoothing is controlled by a reference edge map. Domain transform
filtering is several times faster than dense CRF inference and we show that it
yields comparable semantic segmentation results, accurately capturing object
boundaries. Importantly, our formulation allows learning the reference edge map
from intermediate CNN features instead of using the image gradient magnitude as
in standard DT filtering. This produces task-specific edges in an end-to-end
trainable system optimizing the target semantic segmentation quality. | | Source: | arXiv, 1511.3328 | | Services: | Forum | Review | PDF | Favorites | |
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