  In this paper, we study the problem of semantic part segmentation for
animals. This is more challenging than standard object detection, object
segmentation and pose estimation tasks because semantic parts of animals often
have similar appearance and highly varying shapes. To tackle these challenges,
we build a mixture of compositional models to represent the object boundary and
the boundaries of semantic parts. And we incorporate edge, appearance, and
semantic part cues into the compositional model. Given part-level segmentation
annotation, we develop a novel algorithm to learn a mixture of compositional
models under various poses and viewpoints for certain animal classes.
Furthermore, a linear complexity algorithm is offered for efficient inference
of the compositional model using dynamic programming. We evaluate our method
for horse and cow using a newly annotated dataset on Pascal VOC 2010 which has
pixelwise part labels. Experimental results demonstrate the effectiveness of
our method.
