  Image or object recognition is an important task in computer vision. With the
hight-speed processing power on modern platforms and the availability of mobile
phones everywhere, millions of photos are uploaded to the internet per minute,
it is critical to establish a generic framework for fast and accurate image
processing for automatic recognition and information retrieval. In this paper,
we proposed an efficient image recognition and matching method that is
originally derived from Naive Bayesian classification method to construct a
probabilistic model. Our method support real-time performance and have very
high ability to distinguish similar images with high details. Experiments are
conducted together with intensive comparison with state-of-the-arts on image
matching, such as Ferns recognition and SIFT recognition. The results
demonstrate satisfactory performance.
