Abstract
This paper proposes an optimization approach based on a newly designed parameter set self-evolutionary process. This method improves the discriminability of the original shape descriptor of a 3D model based on spherical harmonics while retaining the efficiency and simplicity of the original shape descriptor. This method captures the critical characteristics, such as the distance, area, and normal distributions of a 3D model extracted along the latitude–longitude directions after the 3D model is normalized in the uniform coordinate frame, and obtains the 3D model׳s features using a spherical harmonics transform. In order to determine the spherical harmonic basis function relationship, an additional weight (0,1) of each spherical harmonic coefficient as random variable is used to search for the optimal variables based on genetic optimization. The resulting transformed features for these random variables are then used as modified shape descriptors. Retrieval performance is examined using the public benchmarks: the Princeton Shape Benchmark, CCCC and NTU databases, and experiments have shown that the optimized additional weight for shape descriptors based on spherical harmonics results in a significant improvement in discriminability.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.