Generalized Local to Global Shape Feature Detection Based on Graph Wavelets
Informative and discriminative feature descriptors are vital in qualitative and quantitative shape analysis for a large variety of graphics applications. Conventional feature descriptors primarily concentrate on discontinuity of certain differential attributes at different orders that naturally give rise to their discriminative power in depicting point, line, small patch features, etc. This paper seeks novel strategies to define generalized, user specified features anywhere on shapes. Our new region based feature descriptors are constructed primarily with the powerful spectral graph wavelets (SGWs) that are both multi scale and multi level in nature, incorporating both local (differential) and global (integral) information. To our best knowledge, this is the first attempt to organize SGWs in a hierarchical way and unite them with the bi harmonic diffusion field towards quantitative region based shape analysis. Furthermore, we develop a local to global shape feature detection framework to facilitate a host of graphics applications, including partial matching without point wise correspondence, coarse to fine recognition, model recognition, etc. Through the extensive experiments and comprehensive comparisons with the state of the art, our framework has exhibited many attractive advantages such as being geometry aware, robust, discriminative, isometry invariant, etc.
IEEE Transactions on Visualization and Computer Graphics