Abstract
3D image retrieval approach is a challenging problem in the research of content-based image retrieval. In this paper, a novel retrieval approach combined differential geometry and co-occurrence matrix is presented. Firstly, Gaussian curvature and mean curvature are utilized to represent the inherent characteristic of spatial surface, and then we use co-occurrence matrix to store the shape information of 3D images. Secondly, normalization process is applied to the co-occurrence matrix and the invariants independence of the translation, scaling, and rotation transforms are proved. In comparison with the recent methods, experiments indicate a lower computation complexity and a better retrieval rate to 3D images with slight different shape characteristic.
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Acknowledgments
This work is supported by Research Fund for the Doctoral Program of Higher Education of China (20090162120069), Science and Technology Plan of Hunan (2009FJ3016), NSFC(61202341), postdoctoral fund of Central South University.
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Guo, K., Duan, G. 3D image retrieval based on differential geometry and co-occurrence matrix. Neural Comput & Applic 24, 715–721 (2014). https://doi.org/10.1007/s00521-012-1288-4
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DOI: https://doi.org/10.1007/s00521-012-1288-4