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

National Innovation of Defense Technology, Academy of Military Sciences PLA China, Beijing 100071, China.
Department of Automation, Tsinghua University, Beijing 100084, China.
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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 recognitionspatial pyramid segmentation

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Tsinghua Science and Technology
Volume 26 Issue 4,
August 2021
Pages 475-483
DOI: 10.26599/TST.2020.9010011
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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