Image Search Result Clustering and Re-Ranking via Partial Grouping

Image search result clustering has become an active research topic. However, due to the limitations of current image search engines, the search result always exhibits partial clustering character, which makes the traditional clustering assumption unreasonable. In this paper, we apply Bregman bubble clustering (BBC), which clusters only a fraction of the whole data set, to image search result clustering. We show that relevant and irrelevant images are less mixed in the clusters produced by BBC. Therefore, we are able to incorporate a cluster based relevance feedback scheme to the clustering result and improve the relevance ranking of the search result according to user´s feedback. Experiments on animal images from Flickr demonstrate the effectiveness of our clustering and re-ranking algorithms.