Efficient near-duplicate image detection by learning from examples

In this paper, we propose a novel scheme for near-duplicate image detection, which is an important problem in variety of applications. While in general content based image retrieval, an image could be similar to the query image in infinitely various ways, the ways in which near-duplicate images deviate from the reference image are very limited. Based on this observation, we proposed to use examplar near-duplicate images, which can be obtained automatically, to improve the performance of near-duplicate image retrieval. We first use examplar near-duplicates to learn an effective distance measure and incorporate the learned metric into locality-sensitive hashing to achieve fast retrieval. We then use examplar near-duplicates to automatically expand the query to further improve the retrieval accuracy. The experimental results validate the effectiveness of the proposed algorithms.