Enhancing Dpf For Near-Replica Image Recognition

Dynamic Partial Function (DPF), which dynamically selects a subset of features to measure pairwise image similarity, has been shown very effective in near-replica image recognition. DPF, however, suffers from the one-size-fits-all problem: it requires that all pairwise similarity measurements must use the same number of features. In this paper, we propose methods for enhancing DPF's performance by allowing different numbers of features to be selected in a pairwise manner. Through extensive empirical studies, we show that our three schemes- Thresholding, Sampling, and Weighting- and hybrid schemes of these three basic approaches, substantially outperform DPF in near-replica image recognition.