With the explosive growth of multimedia data on the internet

the efficient organization and retrieval of large-scale image and video data has become an urgent problem

which expects more efficient low-level feature with low computation. This brings a huge challenge to the conventional visual feature. It is urgent to make descriptor more compact and faster and meanwhile remain robust to many different kinds of image transformation. To this end

we first introduce several schemes of binary features

and then propose a novel fast descriptor for local image patches. A string of binary bits is used

which are derived from the intensity difference quantization between pixel pairs that are sampled according to a fixed random sample pattern. Different with the other binary descriptor approaches

our method first extracts the pixel pairs randomly

and then calculates the intensity differences from these point pairs. We quantize these intensity differences into binary vectors to form the local binary descriptor. Our experiments show that our method is very fast to extract and it shows better more robustness than the other binary feature schemes. The binary descriptor proposed inthis paper is computed very fast and it outperform other binary features on the public datasets we used. It proved that quantization-based method can obtain more robustness than compare-based methods.