IEEE Transactions on Big Data

From the October-December 2015 issue

Learning Visual Semantic Relationships for Efficient Visual Retrieval

By Richang Hong, Yang Yang, Meng Wang, and Xian-Sheng Hua

Featured article thumbnail imageIn this paper, we investigate how to establish the relationship between semantic concepts based on the large-scale real-world click data from image commercial engine, which is a challenging topic because the click data suffers from the noise such as typos, the same concept with different queries, etc. We first define five specific relationships between concepts. We then extract some concept relationship features in textual and visual domain to train the concept relationship models. The relationship of each pair of concepts will thus be classified into one of the five special relationships. We study the efficacy of the conceptual relationships by applying them to augment imperfect image tags, i.e., improve representative power. We further employ a sophisticated hashing approach to transform augmented image tags into binary codes, which are subsequently used for content-based image retrieval task. Experimental results on NUS-WIDE dataset demonstrate the superiority of our proposed approach as compared to state-of-the-art methods.

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Editorials and Announcements


  • We're pleased to announce that Qiang Yang, head of the Huawei Noah's Ark Research Lab and a professor at the Hong Kong University of Science and Technology, has accepted the position of inaugural Editor-in-Chief beginning 1 Jan. 2015. Read more.


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Call for Papers

Special Issue on Data Quality in Big Data: Problems and Solutions

Submission deadline: April 1, 2016. View PDF.

The recent emergence of Big Data ushered in many applications that are vast and varied. Though data quality problems existed before, the advent of Big Data adds new dimensions as well as exacerbates data quality problems. We seek submissions for the September 2016 special issue on Data Quality in Big Data: Problems and Solutions.

The guest editors solicit papers covering all areas of data quality issues in the context of Big Data including data acquisition, data cleaning, semantics and meta data generation, transformations and multi-modal data fusion, data modeling and storage, query execution and workflow optimization, and analytics.

Special Issue on Big Data for Cyber-Physical Systems

Submission deadline: July 15, 2016. View PDF.

Cyber-Physical Systems (CPS) are characterized by the deep complex intertwining among cyber components and physical components. Due to the fast increase in system complexities, the operations of CPS involve sensing, processing and storage of massive amount of data. This nature of “big data” imposes fundamental challenges on the design and management of CPS in multiple aspects such as performance, energy efficiency, security, privacy, reliability, sustainability, fault tolerance, scalability and flexibility. Tackling these challenges necessitates innovative big data techniques for handling massive data in CPS. This special issue will present the state-of-the-art research results on the topic of big data sensing, processing and storage for CPS, and stimulate a broad range of researchers to participate in the interdisciplinary CPS research in the future.

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