@inproceedings{Barz18:AID,
    type = {inproceedings},
    key = {Barz18:AID},
    title = {Automatic Query Image Disambiguation for Content-Based Image Retrieval},
    author = {Björn Barz and Joachim Denzler},
    booktitle = {International Conference on Computer Vision Theory and Applications (VISAPP)},
    year = {2018},
    organization = {INSTICC},
    pages = {249-256},
    publisher = {SciTePress},
    volume = {5},
    abstract = {Query images presented to content-based image retrieval systems often have various different interpretations, making it difficult to identify the search objective pursued by the user. We propose a technique for overcoming this ambiguity, while keeping the amount of required user interaction at a minimum. To achieve this, the neighborhood of the query image is divided into coherent clusters from which the user may choose the relevant ones. A novel feedback integration technique is then employed to re-rank the entire database with regard to both the user feedback and the original query. We evaluate our approach on the publicly available MIRFLICKR-25K dataset, where it leads to a relative improvement of average precision by 23% over the baseline retrieval, which does not distinguish between different image senses.},
    code = {https://github.com/cvjena/aid/},
    doi = {10.5220/0006593402490256},
    isbn = {978-989-758-290-5},
}