@conference {2581,
	title = {Semantic Part Segmentation using Compositional Model combing Shape and Appearance},
	booktitle = {CVPR},
	year = {2015},
	abstract = {<p>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.\&nbsp;\&nbsp; And we incorporate edge,\&nbsp; appearance,\&nbsp; and se- mantic part cues into the compositional model. Given part- level\&nbsp; segmentation\&nbsp; annotation,\&nbsp; we\&nbsp; develop\&nbsp; a\&nbsp; novel\&nbsp; algo- rithm to learn a mixture of compositional models under var- ious poses and viewpoints for certain animal classes.\&nbsp; Fur- thermore, a linear complexity algorithm is offered for effi- cient inference of the compositional model using dynamic programming.\&nbsp; 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.</p>
},
	author = {Jianyu Wang and Alan Yuille}
}
