Analysis of reduced-set construction using image reconstruction from a HOG feature vector

Recently, several methods have been published that demonstrate how to reconstruct an image from a discriminative feature vector. This study explains that previous approaches minimising the histogram-of-oriented-gradient (HOG) feature error in the principal component analysis (PCA) domain of the learning database have a disadvantage in that they cannot reflect the different dynamic range of each PCA dimension, and proposes an improved method to exploit the eigenvalue as the weighting factor of each PCA dimension. Experimental results using pedestrian and vehicle image databases quantitatively show that the proposed method improves the quality of reconstructed images. Additionally, the proposed method is applied to the image reconstruction of the resultant support vectors (SVs) of reduced-set construction which showed the best performance among SV number reduction methods. As the resultant SVs of reduced-set construction are not corresponding to any image of the learning database, it is hard to analyse the problem and performance of the method. By observing the images of the resultant SVs, one potential problem regarding the database used is newly considered and the direction of further study can be established in order to address the problem.