Multi-Query Parallel Field Ranking for image retrieval

Abstract

Relevance feedback image retrieval is an effective scheme bridging the gap between low-level features and high-level concepts. It is essentially a multi-query ranking problem where the user submitted image and provided positive examples are considered as queries. Most of the existing approaches either merge the multiple queries into a single query or consider them independently, and then the geodesic distances on the image manifold are used to measure the similarities between the query image and the other images in database. In this paper, we propose a novel approach called Multi-Query Parallel Field Ranking (MQPFR) which finds an optimal ranking function whose gradient field is as parallel as possible. In this way, the obtained ranking function varies linearly along the geodesics of the data manifold, and achieves the highest value at the multiple queries simultaneously. Extensive experiments are carried out on a large image database and demonstrate the effectiveness of the proposed approach.