Study on shape description under fractional fourier transform

Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (27): 185-188.

• 图形、图像、模式识别 • Previous Articles Next Articles

Study on shape description under fractional fourier transform

ZHANG Wanting1，WANG Bin1，2

1.College of Information Engineering，Nanjing University of Finance and Economics，Nanjing 210046，China
2.Key Laboratory of Electronic Business，Nanjing University of Finance and Economics，Nanjing 210046，China

Received:1900-01-01 Revised:1900-01-01 Online:2011-09-21 Published:2011-09-21

应用分数阶傅里叶变换的形状描述方法研究

张婉婷1，王斌1，2

1.南京财经大学信息工程学院，南京 210046
2.南京财经大学电子商务省级重点实验室，南京 210046

Abstract

Abstract: Fourier descriptor is an essential way for shape description.As a generation of Fourier transform，the fractional Fourier transform has been widely used in the field of digital signal processing projects，but there has been few research reports of applying it to shape analysis.In this paper，it’s the first time to study the method of shape description under fractional Fourier transform，comparing the image retrieval performances of fractional Fourier descriptors with different fractional orders.Retrieval tests are conducted on MPEG-7 contour shape database CE-1.The retrieval results of fractional Fourier descriptors with different orders to different type of shape distortions are fully analyzed.When the order is 0.1，the fractional Fourier descriptor performs worst.With the growth of the order，retrieval results of the fractional Fourier descriptors become better and better.When the order changes to 1.0，the fractional Fourier descriptor performs best.Meanwhile，comparing with the Zernike moment：When the order is 0.1，the Zernike moment gets better result.When the order is 0.1 and 1.0，the fractional Fourier descriptors get better results.

Key words: image retrieval, shape description, Fourier descriptors, fractional Fourier transform

摘要： 傅里叶描述子是一种经典的形状描述方法。作为傅里叶变换的推广形式，分数阶傅里叶变换在数字信号处理工程领域已有相当广泛的应用，但在形状分析领域还很少有研究工作的报道。首次研究了基于分数阶傅里叶变换的形状描述方法，比较了不同阶数下的分数阶傅里叶描述子在图像检索中的性能。通过在MPEG-7的标准图像测试集的图像检索实验，得出：阶数ρ为0.1时，分数阶傅里叶描述子的检索效果最差，随ρ=0.1的增长，检索性能总体呈上升趋势，当ρ=0.5变化到1.0时，检索性能最高。同时，与Zernike矩进行比较：当阶数为0.1时，分数阶傅里叶描述子的检索性能较差;而阶数为0.5、1.0时分数阶傅里叶描述子的检索性能均较好。

关键词: 图像检索, 形状描述, 傅里叶描述子, 分数阶傅里叶变换

ZHANG Wanting1，WANG Bin1，2. Study on shape description under fractional fourier transform[J]. Computer Engineering and Applications, 2011, 47(27): 185-188.

张婉婷1，王斌1，2. 应用分数阶傅里叶变换的形状描述方法研究[J]. 计算机工程与应用, 2011, 47(27): 185-188.

[1]	GUO Hengguang, LIU Wenbiao, YU Renbo. Shape Feature Extraction Using Spike Function [J]. Computer Engineering and Applications, 2021, 57(18): 220-226.
[2]	WU Guanghua, ZHANG Xudong, GE Wei, SUN Ge, MAO Caisheng. Image Copy Detection Based on Model Ensemble and Feature Fusion [J]. Computer Engineering and Applications, 2020, 56(20): 199-205.
[3]	CHEN Xiang, HE Heng, LI Peng, JIN Yu, NIE Lei. Ciphertext Image Retrieval Scheme Based on Target Detection in Cloud Environment [J]. Computer Engineering and Applications, 2020, 56(11): 75-82.
[4]	WEI Qi, WANG Lianming. Shape Feature Extraction and Recognition Based on Multi-Scale Contour Segments [J]. Computer Engineering and Applications, 2019, 55(5): 187-191.
[5]	WANG Miao, JING Junfeng. Image Retrieval Based on Hash Coding and Convolutional Neural Network [J]. Computer Engineering and Applications, 2019, 55(23): 194-199.
[6]	NIU Yaxi, JI Xiaoping. Image Retrieval Algorithm Based on Convolutional Neural Network [J]. Computer Engineering and Applications, 2019, 55(18): 201-206.
[7]	CAI Pengfei, YE Jianfeng. CBIR Method Based on Improved CNN and Bilinear Model [J]. Computer Engineering and Applications, 2019, 55(16): 191-196.
[8]	DUAN Wenjing, CHEN Shaoping. Discrete Hash Image Retrieval Algorithm with High-Level Semantic Features [J]. Computer Engineering and Applications, 2019, 55(13): 212-217.
[9]	ZHENG Dongting, DONG Yun, ZHU Xingchi, ZHOU Yuan. New Spatial Relational Feature for Image Retrieval [J]. Computer Engineering and Applications, 2019, 55(10): 1-8.
[10]	MU Yakun, FENG Shengwei, ZHANG Jing. Image Retrieval Based on Text and Semantic Relevance Analysis [J]. Computer Engineering and Applications, 2019, 55(1): 196-202.
[11]	XIE Guobo, JIANG Xianzhi. Double chaotic image encryption algorithm based on two dimensional discrete fractional Fourier transform [J]. Computer Engineering and Applications, 2018, 54(3): 40-45.
[12]	XIE Guobo, DENG Huajun. Image encryption algorithm based on quantum chaos and fractional Fourier transform [J]. Computer Engineering and Applications, 2018, 54(17): 214-220.
[13]	MIAO Jun1, CUI Song2, DUAN Lijuan2, ZHANG Xuan2, XU Shaowu1. Image retrieval with longest common visual substring [J]. Computer Engineering and Applications, 2018, 54(15): 192-196.
[14]	WANG Qihao1，2, WANG Bin1，2. Local matching algorithm for product image search [J]. Computer Engineering and Applications, 2017, 53(6): 246-251.
[15]	MA Lei1, ZHAO Yongjun1, ZHAO Chuang1, ZHU Jiandong2. Detection and parameter estimation of STLFMCW signal based on periodic FRFT [J]. Computer Engineering and Applications, 2017, 53(1): 134-141.

Study on shape description under fractional fourier transform

应用分数阶傅里叶变换的形状描述方法研究

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics