Semi-supervised hashing for scalable image retrieval

This work proposes a semi-supervised hashing method that is formulated as minimizing empirical error on the labeled data while maximizing variance and independence of hash bits over the labeled and unlabeled data.

Abstract

Large scale image search has recently attracted considerableattentionduetoeasyavailabilityofhugeamountsof data. Several hashing methods have been proposed to allow approximate but highly efficient search. Unsupervised hashing methods show good performance with metric distances but, in image search, semantic similarity is usually givenintermsoflabeledpairsofimages. Thereexistsupervised hashing methods that can handle such semantic similarity but they are prone to overfitting when labeled data is small or noisy. Moreover, these methods are usually very slow to train. In this work, we propose a semi-supervised hashing method that is formulated as minimizing empirical error on the labeled data while maximizing variance and independence of hash bits over the labeled and unlabeleddata. Theproposedmethodcanhandlebothmetricas