|Title||Semantic Part Segmentation using Compositional Model combing Shape and Appearance|
|Publication Type||Conference Paper|
|Year of Publication||2015|
|Authors||Wang, J, Yuille, A|
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.
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