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

Yan Zheng,Yang Song,Baolong Guo,Jie Liu,Ling Wang,Xuebing Zhang,Fanjie Meng

doi:10.1109/access.2020.2994234

Yan Zheng, Yang Song + Show 5 more

Open Access

https://doi.org/10.1109/access.2020.2994234

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Abstract

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.

Highlights

Shape is an important feature in plant leaf retrieval [1], trademark retrieval [2] and object recognition in blurred images
The researches on post-processing methods [3]–[13] in the field of shape retrieval have been extensive for years, many scholars are still working on designing better shape descriptors because they can provide the original dissimilarity/similarity between shapes, which is the basis of shape matching
EXPERIMENTAL RESULTS The MSFDGF-SH-MF, MSFDGF-SH-DS and MSFDGFSH-SF are evaluated in terms of both effectiveness and efficiency

Summary

Introduction

Shape is an important feature in plant leaf retrieval [1], trademark retrieval [2] and object recognition in blurred images. A ineffective shape descriptor cannot obtain high accuracy in shape retrieval, no matter how advanced a post-processing method is combined with. A large number of excellent descriptors [14]–[34] have been proposed These local descriptors, such as SC (shape context) [35], IDSC (inner-distance shape context) [36], TAR (trianglearea representation) [37] and Shape Tree [38], have achieved highly accurate experimental results on the MPEG-7 CE-1 Part B shape database, but they all perform poorly in terms of matching efficiency. The shape descriptor AP&BAP (angular pattern and binary angular pattern) [42] has been proposed to achieve both matching accuracy and efficiency with multiscale description and efficient distance metrics

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