A Pylon Model for Semantic Segmentation
Victor Lempitsky, Andrea Vedaldi and Andrew Zisserman
In: NIPS 2011, December 12-15, 2011, Granada, Spain.
Graph cut optimization is one of the standard workhorses of image segmentation since for
binary random ﬁeld representations of the image, it gives globally optimal results and there
are efﬁcient polynomial time implementations. Often, the random ﬁeld is applied over a
ﬂat partitioning of the image into non-intersecting elements, such as pixels or super-pixels.
In the paper we show that if, instead of a ﬂat partitioning, the image is represented by a
hierarchical segmentation tree, then the resulting energy combining unary and boundary
terms can still be optimized using graph cut (with all the corresponding beneﬁts of global
optimality and efﬁciency). As a result of such inference, the image gets partitioned into a
set of segments that may come from different layers of the tree.
We apply this formulation, which we call the pylon model, to the task of semantic segmentation where the goal is to separate an image into areas belonging to different semantic
classes. The experiments highlight the advantage of inference on a segmentation tree (over
a ﬂat partitioning) and demonstrate that the optimization in the pylon model is able to ﬂexibly choose the level of segmentation across the image. Overall, the proposed system has
superior segmentation accuracy on several datasets (Graz-02, Stanford background) compared to previously suggested approaches.
|EPrint Type:||Conference or Workshop Item (Paper)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Deposited By:||Sunando Sengupta|
|Deposited On:||28 December 2011|