Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation
This addresses the problem of efficient image retrieval for users by reducing reliance on labeled data, though it is incremental as it builds on existing deep hashing techniques.
The authors tackled the challenge of training deep hashing networks for image retrieval without manual annotation by proposing an end-to-end approach that generates binary codes directly from images, using a novel pairwise constrained loss and 3D models for automatic training pair generation, resulting in improvements over state-of-the-art methods on benchmark datasets.
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. 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 learning approach for 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 pairwise 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 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 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.