Bags of phrases with codebooks alignment for near duplicate image detection

Image retrieval from large databases, such as popular social networks, collections of surveillance images and videos, or digital investigation archives, is a very important task for a number of applications. In digital investigation, hashing techniques are commonly used to index large quantities of images to detect copies from different archives. In the last few years, a number of image hashing techniques based on the Bags of Visual Words paradigm have been proposed. Recently, this paradigm has been augmented by using multiple descriptors (Bags of Visual Phrases) to exploit the coherence between different feature spaces. In this paper we propose to further improve the Bags of Visual Phrases approach exploiting the coherence between feature spaces not only in the image representation, but also in the codebooks generation. Experiments performed on real and synthetic near duplicate image datasets show the effectiveness of the proposed approach, which outperforms the original Bags of Visual Phrases approach.