Quadratic-chi similarity metric learning for histogram feature

Abstract

Histogram features, such as SIFT, HOG, LBP et al, are widely used in modern computer vision algorithms. According to [18], chi-square distance is an effective measure for comparing histogram features. In this paper, we propose a new method, named the Quadric-chi similarity metric learning (QCSML) for histogram features. The main contribution of this paper is that we propose a new metric learning method based on chi-square distance, in contrast with traditional Mahalanobis distance metric learning methods. The use of quadric-chi similarity in our method leads to an effective learning algorithm. Our method is tested on SIFT features for face identification, and compared with the state-of-art metric learning method (LDML) on the benchmark dataset, the Labeled Faces in the Wild (LFW). Experimental results show that our method can achieve clear performance gains over LDML.