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Interest Points Detection in Image Based on Topology Features of Multi-level Complex Networks

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Abstract

In this paper, we presents a new method for extracting interest points from RGB images by complex network analysis theory. Firstly, the RGB images are expressed as the multi-level complex network model. The nodes in the complex network model are the maximum degree pixel of subgraphs and the links are the similarity and distance between the maximum degree pixels. Then three different algorithms were proposed to locate these interest points based on three topology features of high-level complex network model, which are degree, closeness and betweenness centrality. In order to verify the effectiveness of our algorithm, we use our algorithm for four different test images. It is remarkable that the identify targets by degree centrality algorithm are similar to Harris and SIFT algorithm, and the accuracy are more than them. The results show that our algorithm could identify interest points from the images effectively.

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Acknowledgements

This work is supported partially by the Jilin Province of China in the Science and Technology Development Plan Project (Grant No. 20170520057JH), partially by the Jilin Province of China in the Science and Technology Development Plan Project (Grant No. 20160418038FG), partially by the Jilin Province of China in the Science and Technology Development Plan Project (Grant No. 20160312017ZX), partially by the Jilin Province department of education Science and Technology Plan Projects (Grant No 201658), partially by the Beihua University Dr. Scientific Research Fund (Grant No. 2015557).

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Authors and Affiliations

  1. College of Electrical and Information Engineering of Beihua University, Jilin, China

    Qingyu Zou & Jing Bai

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  1. Qingyu Zou
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  2. Jing Bai
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Correspondence to Jing Bai.

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Zou, Q., Bai, J. Interest Points Detection in Image Based on Topology Features of Multi-level Complex Networks. Wireless Pers Commun 103, 715–725 (2018). https://doi.org/10.1007/s11277-018-5472-4

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  • DOI: https://doi.org/10.1007/s11277-018-5472-4

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