Sparse Non-Negative Matrix Factorization for Mesh Segmentation

We present a method for 3D mesh segmentation based on sparse non-negative matrix factorization (NMF). Image analysis techniques based on NMF have been shown to decompose images into semantically meaningful local features. Since the features and coefficients are represented in terms of non-negative values, the features contribute to the resulting images in an intuitive, additive fashion. Like spectral mesh segmentation, our method relies on the construction of an affinity matrix which depends on the geometric properties of the mesh. We show that segmentation based on the NMF is simpler to implement, and can result in more meaningful segmentation results than spectral mesh segmentation.

• Type: Journal
• Year: 2016
• Where: IJIG
• Authors: Tim McGraw, Jisun Kang, Donald Herring
• Keywords: IJIG 2016