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Centered convolutional deep Boltzmann machine for 2D shape modeling

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Abstract

An object shape information plays a vital role in many computer applications. Among these applications, some tasks can allow object shape analysis directly solve the problem. Thus, how to extract shape features and model the shape is a crucial issue. This paper proposes a new shape modeling method utilizing the centered convolutional deep Boltzmann machine to model two-dimensional (2D) shape. The proposed method employs a probabilistic generative model based on deep Boltzmann machine and convolution computation to extract both local and global features of the shape within one framework. In addition, we also propose a bidirectional inference-based training algorithm coupled with the centering method to better learn the probability distribution of shapes without the need for a pre-training procedure. Our experimental results show that the proposed model can achieve best shape modeling performance in qualitative and quantitative evaluation compared with other probabilistic generative models, including the restricted Boltzmann machine, convolutional restricted Boltzmann machine, and deep Boltzmann machine.

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Funding

This work was financially supported by the National Natural Science Foundation of China under Grant 41471280, Grant 61701290, Grant 61971273, and Grant 61701289.

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  1. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Jiangong Yang, Shigang Liu & Xili Wang

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  1. Jiangong Yang
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  2. Shigang Liu
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  3. Xili Wang
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Correspondence to Jiangong Yang or Xili Wang.

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Yang, J., Liu, S. & Wang, X. Centered convolutional deep Boltzmann machine for 2D shape modeling. Pers Ubiquit Comput 26, 913–923 (2022). https://doi.org/10.1007/s00779-020-01487-z

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