@conference {2581, title = {Semantic Part Segmentation using Compositional Model combing Shape and Appearance}, booktitle = {CVPR}, year = {2015}, abstract = {

In this paper, we study the problem of semantic part seg- mentation for animals. This is more challenging than stan- dard object detection, object segmentation and pose estima- tion tasks because semantic parts of animals often have sim- ilar 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 seman- tic parts.\ \  And we incorporate edge,\  appearance,\  and se- mantic part cues into the compositional model. Given part- level\  segmentation\  annotation,\  we\  develop\  a\  novel\  algo- rithm to learn a mixture of compositional models under var- ious poses and viewpoints for certain animal classes.\  Fur- thermore, a linear complexity algorithm is offered for effi- cient 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.

}, author = {Jianyu Wang and Alan Yuille} }