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arXiv · 1507.01578

Beyond Semantic Image Segmentation : Exploring Efficient Inference in Video

Subarna Tripathi·Serge Belongie·Truong Nguyen
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Abstract

We explore the efficiency of the CRF inference module beyond image level semantic segmentation. The key idea is to combine the best of two worlds of semantic co-labeling and exploiting more expressive models. Similar to [Alvarez14] our formulation enables us perform inference over ten thousand images within seconds. On the other hand, it can handle higher-order clique potentials similar to [vineet2014] in terms of region-level label consistency and context in terms of co-occurrences. We follow the mean-field updates for higher order potentials similar to [vineet2014] and extend the spatial smoothness and appearance kernels [DenseCRF13] to address video data inspired by [Alvarez14]; thus making the system amenable to perform video semantic segmentation most effectively.

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Subarna Tripathi, Serge Belongie, Truong Nguyen. 2015-07-01. Beyond Semantic Image Segmentation : Exploring Efficient Inference in Video. https://arxiv.org/abs/1507.01578

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