Abstract
Learning compact binary codes for image retrieval task using deep neural networks has attracted increasing attention recently However training deep hashing networks for the task is challenging due to the binary constraints on the hash codes, the similarity preserving property, and the requirement for a vast amount of labelled images. lb the best of our knowledge, none of the existing methods has tackled all of these challenges completely in a uni,fied framework In this work, we propose a nos el end -to -end deep learning approach ffir the task, in which the network is trained to produce binary codes directly from image pixels without the need of manual annotation. In particular to deal with the non -smoothness of binary constraints, we propose a novel pairssise constrained lo.ss function. which simultaneously encodes the distances between pairs of hash codes, and the binary quantization error: In order to train the network with the proposed loss function, we propose an efficient parameter learning algorithm. In addition, to provide similar I dissimilar training images to train the network, ire exploit 3D models reconstructed from unlabelled images for automatic generation of enormous training image pairs. The extensive experiments on image retrieval benchmark datasets demonstrate the improvements of the proposed method over the state-of-the-art compact representation methods on the image retrieval problem.