A Novel Multi-scale Invariant Descriptor Based on Contour and Texture for Shape Recognition

Guo, Ji-shan; Rong, Yi; Gao, Yongsheng; Liu, Ying; Xiong, Shengwu

doi:10.1007/978-3-030-20870-7_34

Cited by 1 publication

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“…Therefore, the study of shape descriptors has never stopped The associate editor coordinating the review of this manuscript and approving it for publication was Shenghong Li. and a large number of excellent descriptors [14]- [34] have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Fourier Transform to Group Feature on Generated Coarser Contours for Fast 2D Shape Matching

et al. 2020

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Fourier descriptors are classical global shape descriptors with high matching speed but low accuracy. To obtain higher accuracy, a novel framework for forming Fourier descriptors is proposed and named as MSFDGF (multiscale Fourier descriptor using group feature). MSFDGF achieves multiscale description by generating coarser contours. Then, a group of complementary features are extracted on the generated coarser contours. Finally, Fourier transform is performed on the features. MSFDGF-SH is a new global descriptor using the MSFDGF framework and shape histograms. Experiments are conducted on four databases, which are MPEG-7 CE-1 Part B, Swedish Plant Leaf, Kimia 99 and Expanded Articulated Database, to evaluate the performance of MSFDGF-SH. The experimental results show that MSFDGF-SH is an effective and efficient global shape descriptor. This new descriptor has a high accuracy of 87.76%, which exceeds the Shape Tree on the MPEG-7 CE-1 Part B dataset. This is the first Fourier descriptor that surpasses the Shape Tree method in terms of both accuracy and speed on this dataset.

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Section: Introductionmentioning

confidence: 99%