Zusammenfassung
Adoption of content-based image retrieval systems (CBIR) requires efficient indexing of the data contents in order to respond to visual queries without explicitly relying on textual keywords. Searching for similar data is closely related to the fundamental problem of nearest neighbor search. Exhaustive comparison of a query across the database is infeasible in large-scale retrieval as it is computationally expensive [1].
Chapter PDF
Similar content being viewed by others
Literatur
Conjeti S, Katouzian A, Kazi A, et al. Metric hashing forests. Med Image Anal. 2016;34:13–29.
Conjeti S, Roy AG, Katouzian A, et al.; Springer. Hashing with residual networks for image retrieval. Proc MICCAI. 2017; p. 541–549.
Conjeti S, Paschali M, Katouzian A, et al. Deep multiple instance hashing for scalable medical image retrieval. Proc MICCAI. 2017; p. 550–558.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer-Verlag GmbH Deutschland
About this paper
Cite this paper
Conjeti, S., Paschali, M., Roy, A.G., Navab, N. (2018). Abstract: Deep Hashing for Large-Scale Medical Image Retrieval. In: Maier, A., Deserno, T., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2018. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56537-7_21
Download citation
DOI: https://doi.org/10.1007/978-3-662-56537-7_21
Published:
Publisher Name: Springer Vieweg, Berlin, Heidelberg
Print ISBN: 978-3-662-56536-0
Online ISBN: 978-3-662-56537-7
eBook Packages: Computer Science and Engineering (German Language)