A Novel Binary Feature from Intensity Difference Quantization between Random Sample of Points

With the explosive growth of web multimedia data, how to manage and retrieval the web-scale data more efficiently has become a urgent problem, which expects more efficient low-level feature with low computation. This pressing need brings a huge challenge to the conventional 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, this paper proposed one kind of fast descriptor for local patch. It consists of a string of binary bits which are derived from the intensity difference quantization (IDQ) between pixel pairs which are chosen according to a fixed random sample pattern, so we called it DIDQ (descriptor based on IDQ). Our experiments show that DIDQ is very fast to be computed and also more robust than the other existing binary represented features.