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A feature binding model in computer vision for object detection

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Abstract

In this paper, the authors propose the “Feature Binding (FB)” strategy in computer vision, a method combined with the biological visual perception theory. Based on feature subspace, the proposed method refers to the biological model and binds features according to certain rules. All features bound in a group are taken as a whole. Besides, all groups with different weight coefficients according to different importance are used to determine the object and its location. The position of the object can be determined based on the calculation according to the corresponding criteria. Feature Binding can significantly enhance the accuracy of object detection and localization. Moreover, the method can accelerate object detection and resist external interference in the unbound feature subspace. Feature Binding has good accuracy not only for the whole object but also for the obscured object. It also has good robustness for different algorithms, which are based on features, including traditional methods and deep learning algorithms. The object positioning system can detect the partially occluded objects more accurately in practice.

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All data, models, and code generated or used during the study appear in the submitted article.

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Funding

This work was supported by the Natural Science Foundation of Jiangsu Higher Education Institutions of China (19KJB520009).

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing Tech University, Nanjing, 211816, China

    Jing Jin & Aichun Zhu

  2. School of Electronic Science and Engineering, Nanjing University, Nanjing, 210023, China

    Yuanqing Wang

  3. Electronic Engineering, School of Engineering, Technological University Dublin, D24 FKT9, Dublin, Ireland

    James Wright

Authors
  1. Jing Jin
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  2. Aichun Zhu
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  3. Yuanqing Wang
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  4. James Wright
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Correspondence to Jing Jin.

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Jin, J., Zhu, A., Wang, Y. et al. A feature binding model in computer vision for object detection. Multimed Tools Appl 80, 19377–19397 (2021). https://doi.org/10.1007/s11042-021-10702-9

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