Learning a Fine Vocabulary

We present a novel similarity measure for bag-of-words type large scale image retrieval. The similarity function is learned in an unsupervised manner, requires no extra space over the standard bag-of-words method and is more discriminative than both L2-based soft assignment and Hamming embedding. Experimentally we show that the novel similarity function achieves mean average precision that is superior to any result published in the literature on the standard Oxford 105k dataset/protocol. At the same time, retrieval with the proposed similarity function is faster than the reference method.