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Query oriented subspace shifting for near-duplicate image detection

Near-duplicate image detection is a critical task in copyright protection. More challenging than the common similarity search, this task requires not only the retrieval of the top similar images but also the detection of the entire near-duplicates collection from the Internet. The common similarity search algorithms are not capable to undertake the latter demand. This paper proposes the query oriented subspace shifting algorithm. The algorithm measures the similarity in various subspaces, which are dynamically generated based on the correlation between samples and the query image. An adaptive threshold is generated automatically to filter the near-duplicates in each subspace. As these subspaces are query oriented, the near-duplicates are less likely to be missed. Experiments shows that this method can effectively improve the detection recall while keeps the similar precision, comparing with the common similarity search algorithm.