Vol.:(0123456789) Artificial Intelligence Review https://doi.org/10.1007/s10462-020-09820-x 1 3 Deep hashing for multi‑label image retrieval: a survey Josiane Rodrigues 1  · Marco Cristo 2  · Juan G. Colonna 2 © Springer Nature B.V. 2020 Abstract Content-based image retrieval (CBIR) aims to display, as a result of a search, images with the same visual contents as a query. This problem has attracted increasing attention in the area of computer vision. Learning-based hashing techniques are amongst the most studied search approaches for approximate nearest neighbors in large-scale image retrieval. With the advance of deep neural networks in image representation, hashing methods for CBIR have started using deep learning to build binary codes. Such strategies are generally known as deep hashing techniques. In this paper, we present a comprehensive deep hashing survey for the task of image retrieval with multiple labels, categorizing the methods according to how the input images are treated: pointwise, pairwise, tripletwise and listwise, as well as their relationships. In addition, we present discussions regarding the cost of space, effi- ciency and search quality of the described models, as well as open issues and future work opportunities. Keywords Content-based image retrieval · Fast similarity search · Hashing · Multi-label learning · Deep learning · Deep hash 1 Introduction The wide availability of images on the web requires the development of effective content representation techniques that allow such images to be retrieved by users. As a result, con- tent-based image retrieval (CBIR), which aims to display as a search result images with the same visual content of a query, has attracted increased attention in the area of computer vision. A variety of efficient search methods have been proposed with the aim of making this task more effective. * Josiane Rodrigues josiane.silva@ifro.edu.br Marco Cristo marco.cristo@icomp.ufam.edu.br Juan G. Colonna juancolonna@icomp.ufam.edu.br 1 Instituto Federal de Rondônia, Porto Velho, Brazil 2 Universidade Federal do Amazonas, Manaus, Brazil