3 research outputs found
Automatic Query Image Disambiguation for Content-Based Image Retrieval
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.Comment: VISAPP 2018 paper, 8 pages, 5 figures. Source code:
https://github.com/cvjena/ai