A Reliability Object Layer for Deep Hashing-based Visual indexing

Abstract:


Nowadays, time-efficient search and retrieval of visually similar content has emerged as a great necessity, while at the same time it constitutes an outstanding research challenge. The latter is further reinforced by the fact that millions of images and videos are generated on a daily basis. In this context, deep hashing techniques, which aim at estimating a very low dimensional binary vector for characterizing each image, have been introduced for realizing realistically fast visual-based search tasks. In this paper, a novel approach to deep hashing is proposed, which explicitly takes into account information about the object types that are present in the image. For achieving this, a novel layer has been introduced on top of current Neural Network (NN) architectures that aims to generate a reliability mask, based on image semantic segmentation information. Thorough experimental evaluation, using four datasets, proves that incorporating local-level information during the hash code learning phase significantly improves the similar retrieval results, compared to state-of-art approaches.


  • K. Gkountakos, T. Semertzidis, G. Th. Papadopoulos and Petros Daras, "A Reliability Object Layer for Deep Hashing-based Visual indexing", 25th International Conference on MultiMedia Modeling (MMM), Thessaloniki, Greece, January 8-11, 2019

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    Contact Information

    Dr. Petros Daras, Principal Researcher Grade Α
    1st km Thermi – Panorama, 57001, Thessaloniki, Greece
    P.O.Box: 60361
    Tel.: +30 2310 464160 (ext. 156)
    Fax: +30 2310 464164
    Email: daras@iti.gr