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

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.

Automated summary

The paper introduces a new approach named HSPOG for image feature extraction, which provides stable feature vectors for images of any size by employing spatial pyramid segmentation and dynamic calculation of pixel sizes, significantly increasing the target detection rate in the image recognition process.