Efficient extraction algorithm for local fuzzy features of dynamic images

Yunsai Chen; Zhao Yang; Liang Ma; Peng Li; Yongjie Pang; Xin Zhao; Wenyi Yang

doi:10.3934/dcdss.2019090

Article Contents

2019, Volume 12, Issue 4&5: 1311-1325. Doi: 10.3934/dcdss.2019090 open access

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Efficient extraction algorithm for local fuzzy features of dynamic images

1.
College of Shipbuilding Engineering, Harbin Engineering University, Harbin, China
2.
Department of Technology, National Deep Sea Center, Qingdao, China
3.
Qingdao National Laboratory for Marine Science and Technology, Scientific and Technology Infranstructure Department, Qingdao, China
4.
Qingdao National Laboratory for Marine Science and Technology, Sharing Platform for Scientific Research Vessels and Infrastructures, Qingdao, China

* Corresponding author: Yunsai Chen
* Corresponding author: Yunsai Chen

Received: May 31, 2017

Revised: October 31, 2017

Published: March 2019

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Abstract

Aiming at the poor extraction effect of the current extraction algorithm for local fuzzy features of dynamic images and the low extraction accuracy, a new algorithm based on FAST corner is proposed to extract the local fuzzy feature of dynamic images efficiently. Through analyzing the mode distortion existing in the local fuzzy features of dynamic images, and processing the spatial domain of dynamic images by using point processing and neighborhood processing, and processing the image frequency domain by filtering, the preprocessing of dynamic images and the effect of local fuzzy feature extraction of dynamic images are improved. On the basis of this, aiming at the shortcomings of FAST corner extraction of local fuzzy features of dynamic images, this paper puts forward the idea of algorithm optimization, and analyzes the realization process of the improved algorithm to achieve the algorithm optimization processing and complete the local fuzzy feature extraction of dynamic images. Based on the least squares method, the inaccurate local fuzzy features in the dynamic images are removed to ensure the accuracy of feature extraction. Experimental results show that the proposed algorithm can accurately extract the local fuzzy features of dynamic images, and the extraction results are better.

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Mathematics Subject Classification: 47B40.

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Figure 1. Construction of the Gaussian pyramid

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Figure 2. FAST feature detection block diagram

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Figure 3. Composition of the eigenvector

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Figure 4. Images used in the experiment

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Figure 5. Preprocessing effect analysis using the proposed algorithm

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Figure 6. Comparison of image feature extraction effect of different algorithms

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References

[1]	W. A. Albukhanajer, J. A. Briffa and Y. Jin, Evolutionary multiobjective image feature extraction in the presence of noise, IEEE Trans Cybern, 45 (2015), 1757-1768.
[2]	C. B., W. J.L., L. C.Q. and et al, Target recognition method via naive bayes combination and simulation sar, Journal of China Academy of Electronics and Information Technology, 73-77.
[3]	J. Bensmail, R. Duvignau and S. Kirgizov, The complexity of deciding whether a graph admits an orientation with fixed weak diameter, Discrete Mathematics and Theoretical Computer Science, 17 (2016), 31-42.
[4]	G. Chen, C. Li and W. Sun, Hyperspectral face recognition via feature extraction and crc-based classifier, Iet Image Processing, 11 (2017), 266-272.
[5]	R. Das, S. Thepade and S. Ghosh, Framework for content-based image identification with standardized multiview features, Etri Journal, 38 (2016), 174-184.
[6]	L. Guan, W. Xie and J. Pei, Segmented Minimum Noise Fraction Transformation for Efficient Feature Extraction of Hyperspectral Images, 10, Elsevier Science Inc., 2015.
[7]	J. M. Guo and H. Prasetyo, Content-based image retrieval using features extracted from halftoning-based block truncation coding, IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 24 (2015), 1010-1024. doi: 10.1109/TIP.2014.2372619.
[8]	M. C. Hu, K. S. Ng, P. Y. Chen, Y. J. Hsiao and C. H. Li, Local binary pattern circuit generator with adjustable parameters for feature extraction, IEEE Transactions on Intelligent Transportation Systems, PP (2017), 1-10.
[9]	U. G. Indahl and T. Naes, Evaluation of alternative spectral feature extraction methods of textural images for multivariate modelling, Journal of Chemometrics, 12 (2015), 261-278.
[10]	S. Jiang, Movement action mark analysis based on body contour feature extraction, Bulletin of Science and Technology, 84-86.
[11]	J. Y. Jung, S. W. Kim, C. H. Yoo, W. J. Park and S. J. Ko, Lbp-ferns-based feature extraction for robust facial recognition, IEEE Transactions on Consumer Electronics, 62 (2017), 446-453.
[12]	P. Knag, J. K. Kim, T. Chen and Z. Zhang, A sparse coding neural network asic with on-chip learning for feature extraction and encoding, IEEE Journal of Solid-State Circuits, 50 (2015), 1070-1079.
[13]	S. Linbo and Q. Huayun, Performance of financial expenditure in china's basic science and math education: Panel data analysis based on ccr model and bbc model, Eurasia Journal of Mathematics Science and Technology Education, 13 (2017), 5217-5224.
[14]	Y. Luo, Y. Wen, D. Tao, J. Gui and C. Xu, Large margin multi-modal multi-task feature extraction for image classification, IEEE Transactions on Image Processing, 25 (2015), 414-427. doi: 10.1109/TIP.2015.2495116.
[15]	A. Tam, J. Barker and D. Rubin, A method for normalizing pathology images to improve feature extraction for quantitative pathology, Medical Physics, 43 (2016), 528-537.
[16]	J. Tang, B. Davvaz, X. Y. Xie and N. Yaqoob, On fuzzy interior -hyperideals in ordered -semihypergroups, Journal of Intelligent & Fuzzy Systems, 32 (2017), 2447-2460.
[17]	H. Wang and S. Song, Image classification based on kcpa feature extraction and rvm, Journal of Jilin University (Science Edition), 357-362.
[18]	W. Wei, Y. Zhang and C. Tian, Latent subclass learning-based unsupervised ensemble feature extraction method for hyperspectral image classification, Remote Sensing Letters, 6 (2015), 257-266.
[19]	F. Y. Wu, Remote sensing image processing based on multi-scale geometric transformation algorithm, Journal of Discrete Mathematical Sciences & Cryptography, 20 (2017), 309-321.
[20]	L. U. Xiao-Ya and D. U. Li-Juan, Fuzzy biological image feature extraction simulation research, Computer Simulation, 397-400.
[21]	Z. Y., D. Y. and Z. X. Y., Image quality assessment based on complementary local feature extraction and quantification, Electronics Letters, 1849-1851.
[22]	L. Yan, J. B. Li, X. Zhu and J. S. Pan, Bilinear discriminant feature line analysis for image feature extraction, Electronics Letters, 51 (2015), 336-338.
[23]	L. Yu, K. Zhou, Y. Yang and H. Chen, Bionic rstn invariant feature extraction method for image recognition and its application, Iet Image Processing, 11 (2017), 227-236.