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Artificial Intelligence Review
Erschienen in: Artificial Intelligence Review 7/2020

27.02.2020

Deep hashing for multi-label image retrieval: a survey

verfasst von: Josiane Rodrigues, Marco Cristo, Juan G. Colonna

Erschienen in: Artificial Intelligence Review | Ausgabe 7/2020

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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, efficiency and search quality of the described models, as well as open issues and future work opportunities.
Vorheriger Artikel A graph-based taxonomy of citation recommendation models
Nächster Artikel An overview of distance and similarity functions for structured data

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Fußnoten
1
Cross-modal is a type of approach that uses two or more different modalities of signal representation as input for neural network (Jiang and Li 2016).
 
2
The Hadamard product is a binary operation between matrices of the same dimension such that \(A = B \odot C\) implies that \(A_{i,j} = B_{i, j} C_{i,j}\).
 
3
The Jaccard coefficient measures the similarity between finite sample sets and is defined as the intersection size divided by the joint size of the sample sets.
 
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Metadaten
Titel
Deep hashing for multi-label image retrieval: a survey
verfasst von
Josiane Rodrigues
Marco Cristo
Juan G. Colonna
Publikationsdatum
27.02.2020
Verlag
Springer Netherlands
Erschienen in
Artificial Intelligence Review / Ausgabe 7/2020
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-020-09820-x

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