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Simultaneous Sparse Dictionary Learning and Pruning
Dictionary learning is a cutting-edge area in imaging processing, that h...
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Computational Intractability of Dictionary Learning for Sparse Representation
In this paper we consider the dictionary learning problem for sparse rep...
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Patch-based Sparse Representation For Bacterial Detection
In this paper, we propose a supervised approach for bacterial detection ...
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A Probabilistic Framework for Discriminative Dictionary Learning
In this paper, we address the problem of discriminative dictionary learn...
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LSALSA: efficient sparse coding in single and multiple dictionary settings
We propose an efficient sparse coding (SC) framework for obtaining spars...
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Hyperspectral and Multispectral Image Fusion based on a Sparse Representation
This paper presents a variational based approach to fusing hyperspectral...
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Dictionary Learning Strategies for Compressed Fiber Sensing Using a Probabilistic Sparse Model
We present a sparse estimation and dictionary learning framework for com...
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Globally Variance-Constrained Sparse Representation for Image Set Compression
Sparse representation presents an efficient approach to approximately recover a signal by the linear composition of a few bases from a learnt dictionary, based on which various successful applications have been observed. However, in the scenario of data compression, its efficiency and popularity are hindered due to the extra overhead for encoding the sparse coefficients. Therefore, how to establish an accurate rate model in sparse coding and dictionary learning becomes meaningful, which has been not fully exploited in the context of sparse representation. According to the Shannon entropy inequality, the variance of data source bounds its entropy, which can reflect the actual coding bits. Hence, in this work a Globally Variance-Constrained Sparse Representation (GVCSR) model is proposed, where a variance-constrained rate model is introduced in the optimization process. Specifically, we employ the Alternating Direction Method of Multipliers (ADMM) to solve the non-convex optimization problem for sparse coding and dictionary learning, both of which have shown state-of-the-art performance in image representation. Furthermore, we investigate the potential of GVCSR in practical image set compression, where a common dictionary is trained by several key images to represent the whole image set. Experimental results have demonstrated significant performance improvements against the most popular image codecs including JPEG and JPEG2000.
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