资 源 简 介
应用背景用Dirichlet分布实现了新的 fisher vector 的实现，相比原来的fisher vector的训练的参数减少，由来有的三个变为一个，提高了训练的效率，同时也提高了训练的正确率。关键技术recent development of image classifications, such as by
SIFT local descriptors. In this paper, we propose a method
to efficiently transform those histogram features for improving
the classification performance. The (L1-normalized)
histogram feature is regarded as a probability mass function,
which is modeled by Dirichlet distribution. Based on
the probabilistic modeling, we induce the Dirichlet Fisher
kernel for transforming the histogram feature vector. The
method works on the individual histogram feature to enhance
the discriminative power at a low computational
cost. On the other hand, in the bag-of-feature (BoF) framework,
the Dirichlet mixture model can be extended to Gaussian
mixture by transforming histogram-based local descriptors,
e.g.,
