Content-based image retrieval with relevance feedback using random walks

In this paper, we propose a novel approach to content-based image retrieval with relevance feedback, which is based on the random walker algorithm introduced in the context of interactive image segmentation. The idea is to treat the relevant and non-relevant images labeled by the user at every feedback round as “seed” nodes for the random walker problem. The ranking score for each unlabeled image is computed as the probability that a random walker starting from that image will reach a relevant seed before encountering a non-relevant one. Our method is easy to implement, parameter-free and scales well to large datasets. Extensive experiments on different real datasets with several image similarity measures show the superiority of our method over different recent approaches.

► We propose an approach to CBIR with relevance feedback based on random walks (RW).
► Graph abstraction: Vertices are images; edges derived from low-level similarities.
► Images labeled by the user are the “seeds” for the random walker algorithm.
► The algorithm is easy to implement, parameter-free and scales to large datasets.