Images are characterized by some statistics of coded vectors in the Bag-of-Features (BOF)model

and then classified by support vector machine (SVM)based on traditional kernel

the existing problems are the loss of discriminant information and choosing of optimal kernel. To solve these problems

we use the multinomial distribution of hard coded vectors or Dirichlet distribution of soft coded vectors as the description of images

and then use maximum likelihood algorithm to estimate the density parameters. Next

the kernel functions between any two images are calculated using a probability product kernel function. Finally

the images are classified by a support vector machine. The experimental results in public image datasets show the proposed algorithm in this paper has achieved better classification performances.