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

[1]  Xuelong Li,et al.  Efficient HOG human detection , 2011, Signal Process..

[2]  Po-Chieh Hung,et al.  Colorimetric calibration in electronic imaging devices using a look-up-table model and interpolations , 1993, J. Electronic Imaging.

[3]  Yu-Jin Zhang,et al.  Fast Human Detection by Boosting Histograms of Oriented Gradients , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[4]  Lin Jing-lianga.  Pedestrian Fast Detection Based on Histograms of Oriented Gradient , 2010 .

[5]  H. Craighead,et al.  Diffractive phase elements based on two-dimensional artificial dielectrics. , 1995, Optics letters.

[6]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Trevor Darrell,et al.  DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.

[9]  Li Long-li.  The conversion of histograms of oriented gradient in different vision-angle and rotation-angle , 2010 .

[10]  郭秉璋.  基於Histogram of Oriented Gradients之課堂舉手辨識研究 , 2012 .

[11]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[12]  Trevor Darrell,et al.  The pyramid match kernel: discriminative classification with sets of image features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[13]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[14]  Changick Kim,et al.  A Hybrid Bags-of-Feature model for Sports Scene Classification , 2015, J. Signal Process. Syst..

[15]  Neha V. Tavari,et al.  Indian Sign Language Recognition based on Histograms of Oriented Gradient , 2014 .