A Fast and Effective Dichotomy Based Hash Algorithm for Image Matching

Multi-view correspondence of wide-baseline image matching is still a challenge task in computer vision. There are two main steps in dealing with correspondence issue: feature description and similarity search. The well-known SIFT descriptor is shown to be a-state-of-art descriptor which could keep distinctive invariant under transformation, large scale changes, noises and even small view point changes. This paper uses the SIFT as feature descriptor, and proposes a new search algorithm for similarity search. The proposed dichotomy based hash (DBH) method performs better than the widely used BBF (Best Bin First) algorithm, and also better than LSH (Local Sensitive Hash). DBH algorithm can obtain much higher (1-precision)-recall ratio in different kinds of image pairs with rotation, scale, noises and weak affine changes. Experimental results show that DBH can obviously improve the search accuracy in a shorter time, and achieve a better coarse match result.