2016 IEEE International Conference on Image Processing (ICIP) 2016
DOI: 10.1109/icip.2016.7532448
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Relative attribute guided dictionary learning

Abstract: A discriminative dictionary learning algorithm is proposed to find sparse signal representations using relative attributes as the available semantic information. In contrast, existing (discriminative) dictionary learning approaches utilize binary label information to enhance the discriminative property of the signal reconstruction residual, the sparse coding vectors or both. Compared to binary attributes or labels, relative attributes contain richer semantic information where the data is annotated with the att… Show more

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“…LRSD is combined with CSD (Class Specific Dictionary) to further improve the discrimination of dictionary space. To make the learnt dictionary have richer semantic information, Babaee et al [6] proposed the RAGDL (Relative Attribute Guided Dictionary Learning) algorithm replacing the binary label information with the relative attribute information. The dictionary construction and learning of these methods are based on sample atoms, leading to the compactness and discrimination of the optimised dictionary, which are closely related to the quality of the original samples.…”
Section: Introductionmentioning
confidence: 99%
“…LRSD is combined with CSD (Class Specific Dictionary) to further improve the discrimination of dictionary space. To make the learnt dictionary have richer semantic information, Babaee et al [6] proposed the RAGDL (Relative Attribute Guided Dictionary Learning) algorithm replacing the binary label information with the relative attribute information. The dictionary construction and learning of these methods are based on sample atoms, leading to the compactness and discrimination of the optimised dictionary, which are closely related to the quality of the original samples.…”
Section: Introductionmentioning
confidence: 99%