Inter-media hashing for large-scale retrieval from heterogeneous data sources

Song, Jingkuan, Yang, Yang, Yang, Yi, Huang, Zi and Shen, Heng Tao (2013). Inter-media hashing for large-scale retrieval from heterogeneous data sources. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. SIGMOD/PODS'13 International Conference on Management of Data, New York, United States, (785-796). 22-27 June 2013. doi:10.1145/2463676.2465274

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Author Song, Jingkuan
Yang, Yang
Yang, Yi
Huang, Zi
Shen, Heng Tao
Title of paper Inter-media hashing for large-scale retrieval from heterogeneous data sources
Conference name SIGMOD/PODS'13 International Conference on Management of Data
Conference location New York, United States
Conference dates 22-27 June 2013
Proceedings title Proceedings of the ACM SIGMOD International Conference on Management of Data   Check publisher's open access policy
Journal name Association for Computing Machinery. Special Interest Group on Management of Data. International Conference Proceedings   Check publisher's open access policy
Place of Publication New York, United States
Publisher Association for Computing Machinery
Publication Year 2013
Sub-type Fully published paper
DOI 10.1145/2463676.2465274
Open Access Status
ISBN 9781450320375
ISSN 0730-8078
Start page 785
End page 796
Total pages 12
Collection year 2014
Language eng
Abstract/Summary In this paper, we present a new multimedia retrieval paradigm to innovate large-scale search of heterogenous multimedia data. It is able to return results of different media types from heterogeneous data sources, e.g., using a query image to retrieve relevant text documents or images from different data sources. This utilizes the widely available data from different sources and caters for the current users' demand of receiving a result list simultaneously containing multiple types of data to obtain a comprehensive understanding of the query's results. To enable large-scale inter-media retrieval, we propose a novel inter-media hashing (IMH) model to explore the correlations among multiple media types from different data sources and tackle the scalability issue. To this end, multimedia data from heterogeneous data sources are transformed into a common Hamming space, in which fast search can be easily implemented by XOR and bit-count operations. Furthermore, we integrate a linear regression model to learn hashing functions so that the hash codes for new data points can be efficiently generated. Experiments conducted on real-world large-scale multimedia datasets demonstrate the superiority of our proposed method compared with state-of-the-art techniques.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Wed, 10 Jul 2013, 13:28:48 EST by Dr Heng Tao Shen on behalf of School of Information Technol and Elec Engineering