Similarity-based partial image retrieval guaranteeing same accuracy as exhaustive matching

We propose a new framework for quick and accurate partial image retrieval from a huge number of images based on a predefined distance measure. Finding partial similarities generally requires a huge amount of storage space for indexes due to the large number of portions of images. The proposed method extracts portions from each database image at a constant spacing, while it extracts all possible portions from a query image. In this way, the proposed method can greatly reduce the size of indexes while theoretically guaranteeing the same accuracy as exhaustive matching.