Ranking Without Learning: Towards Historical Relevance-based Ranking of Social Images

Min Min Chew, Sourav S. Bhowmick, Adam Jatowt

Abstract: Tag-based Social Image Retrieval (TagIR) aims to find relevant social images using keyword queries. State-of-the-art TagIR techniques typically rank query results based on relevance, temporal or popularity criteria. However, these criteria may not always be sufficient to match diverse search intents of users. In this paper, we present a novel ranking scheme that ranks query results (images) based on their historical relevance. Informally, an image is historically relevant if its visual content is relevant to the query and it depicts objects, scenes, or events that are related to human history. To this end, we propose a learning-agnostic technique that leverages Wikipedia to quantify historical relevance of images. We empirically demonstrate the effectiveness of our ranking scheme using Flickr dataset.