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3D shape classification with NNLS coding and optimal projections technique
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DOI：10.1007/s11042-019-08152-5.png)
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Abstract
En 中文
Using graph-based encoding techniques and well-known shape descriptors a framework is presented here which is checked for 3D shape classification performance. A data-driven feature extraction procedure, taking the form of a simple projection operator, is in the core of our framework. Provided a shape database, a graph encapsulating the structural relationships among all the available shapes is first constructed and then is employed to define low-dimensional sparse projections. The weights in the graph, reflecting the database structure, are calculated so as to approximate each shape as a sparse linear combination of the remaining dataset objects. NNLS (nonnegative least squares) coding method is employed and fully exploited in this stage. Sparse coding with L2graph is also included in the framework. By way of solving a generalized eigenanalysis problem, a linear matrix operator is designed by means of optimal projections that will act as the feature extractor. A trained SVM (support vector machine) in the final stage of our framework makes the class prediction. Two popular, inherently high dimensional descriptors, namely ShapeDNA and Global Point Signature (GPS), with a modification emphasizing the smaller eigenvalues are employed in our experimentations with SHREC10, SHREC11 and SCHREC15 datasets. Classification results are very promising and outperform state of the art methods, providing evidence about the highly discriminative nature of the 3D shape representation produced in the proposed multistage framework.
Keywords:
Sparse representation
Nonnegative least squares
3D shape classification
Laplace-Beltrami
Support vector machines
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