  Recently, the Fisher vector representation of local features has attracted
much attention because of its effectiveness in both image classification and
image retrieval. Another trend in the area of image retrieval is the use of
binary features such as ORB, FREAK, and BRISK. Considering the significant
performance improvement for accuracy in both image classification and retrieval
by the Fisher vector of continuous feature descriptors, if the Fisher vector
were also to be applied to binary features, we would receive similar benefits
in binary feature based image retrieval and classification. In this paper, we
derive the closed-form approximation of the Fisher vector of binary features
modeled by the Bernoulli mixture model. We also propose accelerating the Fisher
vector by using the approximate value of posterior probability. Experiments
show that the Fisher vector representation significantly improves the accuracy
of image retrieval compared with a bag of binary words approach.
