Multi-feature structure fusion of contours for unsupervised shape classification

• The different metric replaces the Euclidean metrics for the structure measurement.
• The fusion structure is obtained by the algebraic operation of each structure.
• The fusion structure has the stronger discrimination.

Nonlinear distortion, especially structure distortion, is one of the main reasons for the poor performance of shape contour classification. The structure fusion of multiple features provides a new solution for the structure distortion. How is this structure fusion performed? To answer the question, in this letter, the multi-feature of a contour is defined. Second, the structure of each feature is measured by similarity. Then, the fusion structure is obtained using the algebraic operation of the respective structure, the specific form of which is deduced based on locality-preserving projection (LPP). Finally, the combined feature is mapped into the new structure-fusion feature in terms of the fusion structure. The experiment demonstrates that this structure fusion method is superior to other state-of-the-art methods that address geometrical transformations and nonlinear distortion for classification in Kimia or MPEG-7 datasets.