Common visual codebook generation methods used in
a Bag of Visual words model, e.g. k-means or Gaussian
Mixture Model, use the Euclidean distance to cluster features
into visual code words. However, most popular visual
descriptors are histograms of image measurements. It has
been shown that the Histogram Intersection Kernel (HIK)
is more effective than the Euclidean distance in supervised
learning tasks with histogram features. In this paper, we
demonstrate that HIK can also be used in an unsupervised
manner to significantly improve the generation of visual
codebooks. We propose a histogram kernel k-means algorithm
which is easy to implement and runs almost as fast as
k-means. The HIK codebook has consistently higher recognition
accuracy over k-means codebooks by 2-4%. In addition,
we propose a one-class SVM formulation to create
more effective visual code words which can achieve even
higher accuracy. The proposed method has established new
state-of-the-art performance...
Jianxin Wu, James M. Rehg