期刊
IET IMAGE PROCESSING
卷 11, 期 7, 页码 492-501出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-ipr.2016.0795
关键词
data structures; linear codes; image coding; algebra; multidimensional data representation; basis functions; high-level features; linear tensor coding algorithm; multidimensional data; linear combination; data expansion; linear image coding; multilinear algebra; concrete physical meanings
类别
资金
- National Natural Science Foundation of China [61603218, 61533011, 61572287]
- Shandong Provincial Natural Science Foundation, China [ZR2014HQ054, ZR2015FQ001]
- Ministry of Education of China [20130131120035]
- Grants-in-Aid for Scientific Research [16H01436] Funding Source: KAKEN
Linear coding is widely used to concisely represent data sets by discovering basis functions of capturing high-level features. However, the efficient identification of linear codes for representing multi-dimensional data remains very challenging. In this study, the authors address the problem by proposing a linear tensor coding algorithm to represent multi-dimensional data succinctly via a linear combination of tensor-formed bases without data expansion. Motivated by the amalgamation of linear image coding and multi-linear algebra, each basis function in the authors' algorithm captures some specific variabilities. The basis-associated coefficients can be used for data representation, compression and classification. When the authors apply the algorithm on both simulated phantom data and real facial data, the experimental results demonstrate their algorithm not only preserves the original information of input data, but also produces localised bases with concrete physical meanings.
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