Indexing for efficient processing of noise-free queries

Abstract

A typical query image contains not only relevant objects, but also irrelevant image areas. The latter, referred to as noise, has limited the effectiveness of existing image retrieval systems. In this paper, we propose a technique that allows users to define arbitrary-shaped queries out of example images. We present a new similarity model, and introduce an indexing technique for this new environment. Our query model is more expressive than the standard query-by-example. The user can draw a contour around a number of objects to specify spatial (relative distance) and scaling (relative size) constraints among them, or use separate contours to disassociate these objects. Our experimental results confirm that traditional approaches, such as Local Color Histogram and Correlogram, suffer from noisy queries. In contrast, our method can leverage arbitrary-shaped queries to offer significantly better performance. This is achieved using only a fraction of the storage overhead required by the other two techniques.