Label propagation hashing based on p-stable distribution and coordinate descent

Hashing is a useful tool for contents-based image retrieval on large scale database. This paper presents an unsupervised data-dependent hashing method which learns similarity preserving binary codes. It uses p-stable distribution and coordinate descent method to achieve a good approximate solution for an acknowledged objective of hashing. This method consists of two steps. Firstly, it uses p-stable distribution properties to generate an initial partial hashing solution. Next, coordinate descent method is used to extend this partial solution to be complete. Our approach combines the advantages of both data-independent and data-dependent methods, which makes full use of the training data, requires reduced training time, and is easy to implement. Experiments show that our method outperforms several other state-of-the-art methods.