Parameter setting of self-quotient ε-filter using HOG feature distance
Mitsuharu Matsumoto · 2011
This paper describes parameter setting of self-quotient ε-filter (SQEF) using Histograms of Oriented Gradients (HOG) feature distance. Parameter setting problem is generally solved by maximization or minimization of some objective evaluation functions such as correlation and statistical independence. However, it is not always easy to set such objective evaluation functions when we handle feature extracted images like SQEF because it is difficult to evaluate whether the parameter is optimal or not. On the other hand, even when we cannot employ objective assumptions, we sometimes know that an image includes some subjective information. Based on the above prospects, we consider HOG feature vectors of self-quotient filter (SQF) and SQEF of human images, and propose feature distance based parameter setting to use the subjective information. Experimental results show that the proposed approach has a potential to handle the parameter setting of feature extraction filter.