  Proximity Distribution Kernel is an effective method for bag-of-featues based
image representation. In this paper, we investigate the soft assignment of
visual words to image features for proximity distribution. Visual word
contribution function is proposed to model ambiguous proximity distributions.
Three ambiguous proximity distributions is developed by three ambiguous
contribution functions. The experiments are conducted on both classification
and retrieval of medical image data sets. The results show that the performance
of the proposed methods, Proximity Distribution Kernel (PDK), is better or
comparable to the state-of-the-art bag-of-features based image representation
methods.
