LHI-tree: An efficient disk-based image search application

Querying of nearest neighbour (NN) elements on large data collections is an important task for several information or content retrieval tasks. In the paper Local Hash-indexing tree (LHI-tree) is introduced, which is a disk-based index scheme that uses RAM for quick space partition localization and hard disks for the hash indexing. When large collections are considered, such hybrid data structure should be used to implement an effective indexing service. The proposed structure can produce list of approximate nearest neighbours. We compare LHI-tree to FLANN (Fast Library for Approximate Nearest Neighbors), an effective and frequently instanced implementation of ANN search. We show that they produce similar lists of retrieved images (although FLANN works only on RAM). In case of huge multimedia database a disk based indexing and retrieval method has a significant advantage against a vector based system running in RAM data. For the visual content indexing we built an image descriptor composed of four different information representations: edge histogram, entropy histogram, pattern histogram and dominant component colour characteristics. The paper will mention the content based retrieval of Hungarian Wikipedia images.