Low-Dimensional Histogram of Oriented Gradients with Non-Overlapping Scheme
Huo Ya · 2014
As a derivation version of scale-invariant feature transform( SIFT),histogram of oriented gradients( HOG) is widely used in human detection,gesture recognition,face recognition,scene classification,etc. However,the high dimension of the HOG feature vector leads to the curse of the dimensionality and high computation complexity. In this paper,it is found that the high dimension of HOG feature vector results from computing histograms of overlapping blocks. Though overlapping block is useful for enhancing the robustness,it leads to redundant information. To reduce the redundant information and the number of features as well,a non-overlapping version of HOG is proposed. The dimensions of the proposed method are 1 /3 of those of traditional ones. The experimental results on palm and human detection demonstrate the efficiency and effectiveness of the proposed method.