Similarity-Preserving Binary Hashing for Image Retrieval in large databases

Hashing techniques have become very popular to solve the content-based image retrieval problem in gigantic image databases because they allow to represent feature vectors using compact binary codes. Binary codes provide speed and are memory-efficient. Different approaches have been taken by researchers, some of them based on the Spectral Hashing objective function, among these the recently proposed Anchor Graph Hashing. In this paper an extension to the Anchor Graph Hashing technique which deals with supervised/label information is proposed. This extension is based on representing the samples in an intermediate semantic space that comes from the definition of an equivalence relation in a intermediate geometric hashing. The results show that our approach is a very effective way to incorporate such supervised information to the Anchor Graph Hashing method. On the other hand, the results show that our approach is very effective to deal with clean supervised information but still some further efforts are required in those scenarios where the label information has important presence of noise.