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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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




    Publication date :

    2021-07-11


    Size :

    1788477 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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

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


    Semantic Modeling of Natural Scenes for Content-Based Image Retrieval

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