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Tsinghua Science and Technology 2021, 26(4): 475-483 https://doi.org/10.26599/TST.2020.9010011
Open Access | Issue | Published: 04 January 2021

HSPOG: An Optimized Target Recognition Method Based on Histogram of Spatial Pyramid Oriented Gradients

Show Author's Information Shaojun Guo,  Feng Liu,  Xiaohu Yuan,  Chunrong Zou,  Li Chen,  Tongsheng Shen( )
National Innovation of Defense Technology, Academy of Military Sciences PLA China, Beijing 100071, China.
Department of Automation, Tsinghua University, Beijing 100084, China.
Keywords:
Histograms of Oriented Gradients (HOG), Histogram of Spatial Pyramid Oriented Gradients (HSPOG), object recognition, spatial pyramid segmentation
Cite this article:
Guo S, Liu F, Yuan X, et al. HSPOG: An Optimized Target Recognition Method Based on Histogram of Spatial Pyramid Oriented Gradients. Tsinghua Science and Technology, 2021, 26(4): 475-483. https://doi.org/10.26599/TST.2020.9010011
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Abstract Full text About this article

The Histograms of Oriented Gradients (HOG) can produce good results in an image target recognition mission, but it requires the same size of the target images for classification of inputs. In response to this shortcoming, this paper performs spatial pyramid segmentation on target images of any size, gets the pixel size of each image block dynamically, and further calculates and normalizes the gradient of the oriented feature of each block region in each image layer. The new feature is called the Histogram of Spatial Pyramid Oriented Gradients (HSPOG). This approach can obtain stable vectors for images of any size, and increase the target detection rate in the image recognition process significantly. Finally, the article verifies the algorithm using VOC2012 image data and compares the effect of HOG.


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About this article

HSPOG: An Optimized Target Recognition Method Based on Histogram of Spatial Pyramid Oriented Gradients

Show Author's information Shaojun Guo, Feng Liu, Xiaohu Yuan, Chunrong Zou, Li Chen, Tongsheng Shen( )
National Innovation of Defense Technology, Academy of Military Sciences PLA China, Beijing 100071, China.
Department of Automation, Tsinghua University, Beijing 100084, China.

Abstract

The Histograms of Oriented Gradients (HOG) can produce good results in an image target recognition mission, but it requires the same size of the target images for classification of inputs. In response to this shortcoming, this paper performs spatial pyramid segmentation on target images of any size, gets the pixel size of each image block dynamically, and further calculates and normalizes the gradient of the oriented feature of each block region in each image layer. The new feature is called the Histogram of Spatial Pyramid Oriented Gradients (HSPOG). This approach can obtain stable vectors for images of any size, and increase the target detection rate in the image recognition process significantly. Finally, the article verifies the algorithm using VOC2012 image data and compares the effect of HOG.

Keywords: Histograms of Oriented Gradients (HOG), Histogram of Spatial Pyramid Oriented Gradients (HSPOG), object recognition, spatial pyramid segmentation

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Publication history

Received: 13 March 2020
Accepted: 29 March 2020
Published: 04 January 2021
Issue date: August 2021

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© The author(s) 2021

Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (No. 51802348).

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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