Negative Correlation Discovery for Big Multimedia Data Semantic Concept Mining and Retrieval

Abstract

With massive amounts of data producing each day in almost every field, traditional data processing techniques have become more and more inadequate. However, the research of effectively managing and retrieving these big data is still under development. Multimedia high-level semantic concept mining and retrieval in big data is one of the most challenging research topics, which requires joint efforts from researchers in both big data mining and multimedia domains. In order to bridge the semantic gap between high-level concepts and low-level visual features, correlation discovery in semantic concept mining is worth exploring. Meanwhile, correlation discovery is a computationally intensive task in the sense that it requires a deep analysis of very large and growing repositories. This paper presents a novel system of discovering negative correlation for semantic concept mining and retrieval. It is designed to adapt to Hadoop MapReduce framework, which is further extended to utilize Spark, a more efficient and general cluster computing engine. The experimental results demonstrate the feasibility of utilizing big data technologies in negative correlation discovery.