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Hashing with Residual Networks for Image Retrieval

  • Sailesh ConjetiEmail author
  • Abhijit Guha Roy
  • Amin Katouzian
  • Nassir Navab
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

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.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sailesh Conjeti
    • 1
    Email author
  • Abhijit Guha Roy
    • 1
  • Amin Katouzian
    • 2
  • Nassir Navab
    • 1
    • 3
  1. 1.Computer Aided Medical ProceduresTechnische Universität MünchenMunichGermany
  2. 2.IBM Almaden Research CenterAlmadenUSA
  3. 3.Computer Aided Medical ProceduresJohns Hopkins UniversityBaltimoreUSA

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