Gabriela Csurka, Florent Perronnin
British Machine Vision Conference, Leeds, UK, Sept 1-4, 2008
We propose a simple approach to semantic image segmentation. Our
system scores low-level patches according to their class relevance, propagates
these posterior probabilities to pixels and uses low-level segmentation
to guide the semantic segmentation. The two main contributions of this paper
are as follows. First, for the patch scoring, we describe each patch with
a high-level descriptor based on the Fisher kernel and use a set of linear
classifiers. While the Fisher kernel methodology was shown to lead to high
accuracy for image classification, it has not been applied to the segmentation
problem. Second, we use global image classifiers to take into account the
context of the objects to be segmented. If an image as a whole is unlikely to
contain an object class, then the corresponding class is not considered in the
segmentation pipeline. This increases the classification accuracy and reduces
the computational cost. We will show that despite its apparent simplicity, this
system provides above state-of-the-art performance on the PASCAL VOC
2007 dataset and state-of-the-art performance on the MSRC 21 dataset.
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