Most of the content-based image retrieval systems require a distance computation for each candidate image in the database. As a brute-force approach, the exhaustive search can be employed for this computation. However, this exhaustive search is time-consuming and limits the usefulness of such systems. Thus, there is a growing demand for a fast algorithm which provides the same retrieval results as the exhaustive search. We propose a fast search algorithm based on a multiresolution data structure. The proposed algorithm computes the lower bound of distance at each level and compares it with the latest minimum distance, starting from the low-resolution level. Once it is larger than the latest minimum distance, we can remove the candidates without calculating the full-resolution distance. By doing this, we can dramatically reduce the total computational complexity. It is noticeable that the proposed fast algorithm provides not only the same retrieval results as the exhaustive search, but also a faster searching ability than existing fast algorithms. For additional performance improvement, we can easily combine the proposed algorithm with existing tree-based algorithms. The algorithm can also be used for the fast matching of various features such as luminance histograms, edge histograms, and local binary partition textures.
['Byung Cheol Song', 'Myung Jun Kim', 'Jong Beom Ra']
A fast multiresolution feature matching algorithm for exhaustive search in large image databases