Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation

Learning compact binary codes for image retrieval problem using deep neural networks has attracted increasing attention recently. However, training deep hashing networks 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. To the best of our knowledge, none of the existing methods has tackled all of these challenges completely in a unified framework. In this work, we propose a novel end-to-end deep hashing approach, which is trained to produce binary codes directly from image pixels without the need of manual annotation. In particular, we propose a novel pairwise binary constrained loss 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 also propose an efficient parameter learning algorithm. In addition, to provide similar/dissimilar training images to train the network, we exploit 3D models reconstructed from unlabelled images for automatic generation of enormous similar/dissimilar pairs. Extensive experiments on three image retrieval benchmark datasets demonstrate the superior performance of the proposed method over the state-of-the-art hashing methods on the image retrieval problem.