In 3D model retrieval, feature extraction of 3D model is a very important topic. In this paper, the goal of the proposed approach is to decompose 3D model to meaningful components by iterative scheme and hierarchical fuzzy clustering, then construct a node graph to represent 3D model. The similarity calculation between two 3D models is processed using a coarse-to-fine strategy. Experiment results show that out method is suitable for part matching, articulated matching and global matching of the models.
In this paper, we propose a new method for finding similarity in 3-D protein structure comparison. Different from the other existing methods, our method is grounded in the theory of fractal geometry. The proposed feature vectors of protein structures are invariant to the rotation, translation, scaling of the protein molecule, and it is simple to implement. The method is very fast because it requires neither alignment of the chains nor any chain-chain comparison. We calculate the fractal features of a set of 200 protein structures selected from PDB (Protein Data Bank). The experimental result shows that our method is very effective in classification of 3-D protein structures.
In molecular biology, retrieval of 3D protein structures is a very important topic. In this paper. From the view of geometry, we propose three types of characteristics (e.g. VDP,DDP,ADP) for describing 3D protein structures in order to perform similarity search and classification. The goal of the approach is to reduce the 3D protein structures matching problem to the comparison of the probability distribution. Experimental results show that our method is robust and invariant with respect to translation, rotation.