Learning to re-rank
Vidit Jain, Manik Varma · 2011
Our objective is to improve the performance of keyword based image search engines by re-ranking their original results. To this end, we address three limitations of existing search engines in this paper. First, there is no straight-forward, fully automated way of going from textual queries to visual features. Image search engines therefore primarily rely on static and textual features for ranking. Visual features are mainly used for secondary tasks such as finding similar images. Second, image rankers are trained on query-image pairs labeled with relevance judgments determined by human experts. Such labels are well known to be noisy due to various factors including ambiguous queries, unknown user intent and subjectivity in human judgments. This leads to learning a sub-optimal ranker. Finally, a static ranker is typically built to handle disparate user queries. The ranker is therefore unable to adapt its parameters to suit the query at hand which again leads to sub-optimal results. We demonstrate that all of these problems can be mitigated by employing a re-ranking algorithm that leverages aggregate user click data.