Semantic Part Segmentation Using Compositional Model Combining Shape and Appearance

Jianyu Wang, Alan L. Yuille; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1788-1797

Abstract


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.

Related Material


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[bibtex]
@InProceedings{Wang_2015_CVPR,
author = {Wang, Jianyu and Yuille, Alan L.},
title = {Semantic Part Segmentation Using Compositional Model Combining Shape and Appearance},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}