![](bg4.png)
Figure 3: More matching of results.
We have summarized recognition rates for some
different approaches which are tested on the ETH-80
database and cited in (Daliri et al., 2009).
Table 1: Some recognition rates for different algorithms
tested on ETH-80 database.
Algorithm Recognition rate (%)
SC greedy 86.40
Decision tree 93.02
Fragment-based
approach
86.40
Kernel-edit-distance 91.33
Robust symbolic
representation
89.03
Proposed algorithm 92.50
5 CONCLUSIONS
In this paper, we have presented a new approach to
represent the shape of the projection of a 3D object
which enables similarity search. A key characteristic
of our approach is the use of the geometric
description of different parts constituting the outer
closed boundary of the shape using a set of cubic
curves. These curves enable us comparisons between
different shapes. A shape matching technique, using
the Hausdorff distance between two curves has been
proposed. In our experiments, we have demonstrated
invariance to similarity transformations: rotation and
scaling. The results are encouraging. The proposed
approach achieves a recognition rate equal to 92.5%.
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