Hashing with residual networks for image retrieval

Conjeti S, Roy AG, Katouzian A, Navab N (2017)

Publication Type: Conference contribution

Publication year: 2017

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 10435 LNCS

Pages Range: 541-549

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Quebec City, QC, CAN

ISBN: 9783319661780

DOI: 10.1007/978-3-319-66179-7_62

Abstract

We propose a novel deeply learnt convolutional neural network architecture for supervised hashing of medical images through residual learning, coined as Deep Residual Hashing (DRH). It offers maximal separability of classes in hashing space while preserving semantic similarities in local embedding neighborhoods. We also introduce a new optimization formulation comprising of complementary loss terms and regularizations that suit hashing objectives the best by controlling over quantization errors. We conduct extensive validations on 2,599 Chest X-ray images with co-morbidities against eight state-of-the-art hashing techniques and demonstrate improved performance and computational benefits of the proposed algorithm for fast and scalable retrieval.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) IBM Research - Almaden US United States (USA) (US)

How to cite

APA:

Conjeti, S., Roy, A.G., Katouzian, A., & Navab, N. (2017). Hashing with residual networks for image retrieval. In Lena Maier-Hein, Alfred Franz, Pierre Jannin, Simon Duchesne, Maxime Descoteaux, D. Louis Collins (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 541-549). Quebec City, QC, CAN: Springer Verlag.

MLA:

Conjeti, Sailesh, et al. "Hashing with residual networks for image retrieval." Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, Quebec City, QC, CAN Ed. Lena Maier-Hein, Alfred Franz, Pierre Jannin, Simon Duchesne, Maxime Descoteaux, D. Louis Collins, Springer Verlag, 2017. 541-549.

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