Although Content Based Image Retrieval (CBIR) is an active research field, application to images simultaneously containing multiple objects has received limited research inter- est. For such complex images, it is difficult to precisely convey the query intention, to encode all the image aspects into one compact global feature representation and to unambiguously define label similarity or dissimilarity. Motivated by the recent success on many visual benchmark tasks, we propose a self- supervised method to train a feature representation learning model. We propose usage of multiple query images, and use an attention based architecture to extract features from diverse image aspects that benefits from this. The method shows promising performance on road scene datasets, and, consistently improves when multiple query images are used instead of a single query image.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Self-Supervised Representation Learning for Content Based Image Retrieval of Complex Scenes


    Beteiligte:


    Erscheinungsdatum :

    2021-07-11


    Format / Umfang :

    1788477 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Semantic Modeling of Natural Scenes for Content-Based Image Retrieval

    Vogel, J. / Schiele, B. | British Library Online Contents | 2007


    Localized Content-Based Image Retrieval Using Semi-Supervised Multiple Instance Learning

    Zhang, Dan / Shi, Zhenwei / Song, Yangqiu et al. | Springer Verlag | 2007


    Deformation tolerant generalized Hough transform for sketch-based image retrieval in complex scenes

    Anelli, M. / Cinque, L. / Sangineto, E. | British Library Online Contents | 2007


    Content-based image retrieval using multiresolution histogram representation [2606-30]

    Vellaikal, A. / Kuo, C.-C. J. / SPIE | British Library Conference Proceedings | 1995