Accelerated Knowledge-Driven Image Mining System for Data Fusion in Big Data

Abstract

In this paper, we present a knowledge-driven content-based information mining system for data fusion in Big Data. The tool combines, at pixel level, the unsupervised clustering results of different number of features, extracted from different image types, with a user given semantic concepts in order to calculate the posterior probability that allows the final search. The system is able to learn different semantic labels based on Bayesian networks and retrieve the related images with only a few user interactions, greatly optimizing the computational costs and over performing existing similar systems in various orders of magnitude.