Efficient indexing for strongly similar subimage retrieval

Strongly similar subimages contain different views of the same object. In subimage search, the user selects an image region and the retrieval system attempts to find matching subimages in an image database that are strongly similar. Solutions have been proposed using salient features or "interest points" that have associated descriptor vectors. However, searching large image databases by exhaustive comparison of interest point descriptors is not feasible. To solve this problem, we propose a novel off-line indexing scheme based on the most significant bits (MSBs) of these descriptors. On-line search uses this index file to limit the search to interest points whose descriptors have the same MSB value, a process up to three orders of magnitude faster than exhaustive search. It is also incremental, since the index file for a union of a group of images can be created by merging the index files of the individual image groups. The effectiveness of the approach is demonstrated experimentally on a variety of image databases.