Negative Correlation Discovery for Big Multimedia Data Semantic Concept Mining and Retrieval
Negative Correlation Discovery for Big Multimedia Data Semantic Concept Mining and Retrieval
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DOI：
10.1109/icsc.2016.73
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
Yilin Yan;Mei-Ling Shyu;Qiusha Zhu
中科院分区：
文献类型：
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作者：
Yilin Yan;Mei-Ling Shyu;Qiusha Zhu
几乎每个领域每天都产生大量的数据，传统的数据处理技术已经变得越来越不够用。然而，有效管理和检索这些大数据的研究仍在发展中。大数据中多媒体高层语义概念的挖掘与检索是当前最具挑战性的研究课题之一，需要大数据挖掘和多媒体领域的研究人员共同努力。为了弥合高层概念和底层视觉特征之间的语义鸿沟，语义概念挖掘中的关联发现是一个值得探索的问题。同时，相关性发现是一项计算密集型任务，因为它需要对非常大且不断增长的存储库进行深入分析。本文提出了一种新的语义概念挖掘和检索的负相关发现系统。它的设计是为了适应Hadoop MapReduce框架，进一步扩展到利用Spark，一个更高效和通用的集群计算引擎。实验结果证明了利用大数据技术进行负相关发现的可行性。
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.