Fast Image Index for Database Management Engines

Abstract

Large-scale image repositories are challenging to perform queries based on the content of the images. The paper proposes a novel, nested-dictionary data structure for indexing image local features. The method transforms image local feature vectors into two-level hashes and builds an index of the content of the images in the database. The algorithm can be used in database management systems. We implemented it with an example image descriptor and deployed in a relational database. We performed the experiments on two image large benchmark datasets.

A Novel Grid-Based Clustering Algorithm

“…A wide of large collections of data presents a great challenge to clustering algorithms, so a lot of new different clustering algorithms and their configurations are being intensively developed, e.g. [8,10,11]. However, there is no clustering algorithm which creates the right clusters for all datasets.…”