Result: Two-dimensional relaxed representation
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Further Information
In this paper, a novel classification framework called two-dimensional relaxed representation (2DRR) is proposed for image classification. Different from recent popular vector-based representations with/ without sparsity which encode a vector signal as a sparse/nonsparse linear combination of elementary vector signals, 2DRR is based on 2D image matrices, where each column of the input matrix signal is represented by a combination of the corresponding columns of the elementary matrices. In order to preserve the global linear coding relationship between the input matrix and these elementary matrices, the proposed 2DRR constrains the coding coefficients corresponding to each column of the input matrix to be locally close. Then two algorithms are derived from the 2DRR framework under the l2 norm and the l1 norm respectively. Extensive experimental results show the effectiveness of the proposed algorithms in comparison to three existing algorithms.