Fast Near-Duplicate Image Detection Using Uniform Randomized Trees

Abstract

Indexing structure plays an important role in the application of fast near-duplicate image detection, since it can narrow down the search space. In this article, we develop a cluster of uniform randomized trees (URTs) as an efficient indexing structure to perform fast near-duplicate image detection. The main contribution in this article is that we introduce “uniformity” and “randomness” into the indexing construction. The uniformity requires classifying the object images into the same scale subsets. Such a decision makes good use of the two facts in near-duplicate image detection, namely: (1) the number of categories is huge; (2) a single category usually contains only a small number of images. Therefore, the uniform distribution is very beneficial to narrow down the search space and does not significantly degrade the detection accuracy. The randomness is embedded into the generation of feature subspace and projection direction, improveing the flexibility of indexing construction. The experimental results show that the proposed method is more efficient than the popular locality-sensitive hashing and more stable and flexible than the traditional KD-tree.