Hash Functions for Near Duplicate Image Retrieval

This paper proposes new hash functions for indexing local image descriptors. These functions are first applied and evaluated as a range neighbor algorithm. We show that it obtains similar results as several state of the art algorithms. In the context of near duplicate image retrieval, we integrated the proposed hash functions within a bag of words approach. Because most of the other methods use a kmeans-based vocabulary, they require an off-line learning stage and highest performance is obtained when the vocabulary is learned on the searched database. For application where images are often added or removed from the searched dataset, the learning stage must be repeated regularly in order to keep high recalls. We show that our hash functions in a bag of words approach has similar recalls as bag of words where vocabulary is learnt on the searched dataset, but our method does not require any learning stage. It is thus very well adapted to near duplicate image retrieval applications where the dataset evolves regularly.