Abstract
With the rapid development of computer and information technology, the applications of the image recognition become more and more universal. As the basis of the image processing, the image representation plays an increasingly important role in computer vision and image recognition. In recent years, complex network theory has aroused the interest of many researchers. Most of the complex network features of images are statistical features, which not only have good stability, but also have strong anti-noise ability. A directed complex network representation model is proposed in this paper, which uses K-Nearest Neighbor (KNN) method to give an evolution method of directed complex networks. Finally, the feature description of the image is completed and the recognition of the image is realized by extracting the directed complex network features at different evolutionary times.
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Backes, A.R., Casanova, D., Bruno, O.M.: A complex network-based approach for boundary shape analysis. Pattern Recogn. 42(1), 54–67 (2009)
Bai, X., Wang, B., Wang, X., Liu, W., Tu, Z.: Co-transduction for shape retrieval. IEEE Trans. Image Process. 21(5), 2747–2757 (2012)
Bai, X., Fang, Y., Lin, W., Wang, L.: Saliency-based defect detection in industrial images by using phase spectrum. IEEE Trans. Ind. Inform. 10(4), 2135–2145 (2014)
Bhowmik, M.K., Shil, S., Saha, P.: Feature points extraction of thermal face using harris interest point detection. Procedia Technol. 10(1), 724–730 (2013)
Gao, X., Xiao, B., Tao, D., Li, X.: Image categorization: graph edit direction histogram. Pattern Recogn. 41(10), 3179–3191 (2008)
Ker, J., Wang, L., Rao, J., Lim, T.: Deep learning applications in medical image analysis. IEEE Access 6, 9375–9389 (2018)
Luo, B., Wilson, R.C., Hancock, E.R.: Spectral embedding of graphs. Pattern Recogn. 36(10), 2213–2230 (2003)
Ratle, F., Camps-Valls, G., Weston, J.: Semisupervised neural networks for efficient hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 48(5), 2271–2282 (2010)
Shu, X., Wu, X.J.: A novel contour descriptor for 2D shape matching and its application to image retrieval. Image Vis. Comput. 29(4), 286–294 (2011)
Tang, J., Jiang, B., Chang, C.C., Luo, B.: Graph structure analysis based on complex network. Digit. Signal Process. 22(5), 713–725 (2012)
Wei, X., Yang, Z., Liu, Y., Wei, D., Jia, L., Li, Y.: Railway track fastener defect detection based on image processing and deep learning techniques: a comparative study. Eng. Appl. Artif. Intell. 80, 66–81 (2019)
Zhang, L., Wang, L., Lin, W.: Semisupervised biased maximum margin analysis for interactive image retrieval. IEEE Trans. Image Process. 21(4), 2294–2308 (2012)
Zhang, L., Wang, L., Lin, W.: Conjunctive patches subspace learning with side information for collaborative image retrieval. IEEE Trans. Image Process. 21(8), 3707–3720 (2012)
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Shi, S. (2020). Image Recognition Based on Directed Complex Network Model. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_52
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DOI: https://doi.org/10.1007/978-3-030-32591-6_52
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