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This paper explores novel approaches for improving the spatial codification
for the pooling of local descriptors to solve the semantic
segmentation problem. We propose to partition the image into three
regions for each object to be described: Figure, Border and Ground.
This partition aims at minimizing the influence of the image context
on the object description and vice versa by introducing an intermediate
zone around the object contour. Furthermore, we also propose a
richer visual descriptor of the object by applying a Spatial Pyramid
over the Figure region. Two novel Spatial Pyramid configurations
are explored: Cartesian-based and crown-based Spatial Pyramids.
We test these approaches with state-of-the-art techniques and show
that they improve the Figure-Ground based pooling in the Pascal
VOC 2011 and 2012 semantic segmentation challenges.
CitationVentura, C., Giro, X., Vilaplana, V. Improving spatial codification in semantic segmentation. A: IEEE International Conference on Image Processing. "Proceedings International Conference on Image Processing". Quebec: 2015.
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