Research on software credibility algorithm based on deep convolutional sparse coding [PDF]
MATEC Web of Conferences, 2021 Based on the author's research time, this paper studies the software credibility algorithm based on deep convolutional sparse coding. Firstly, it summarizes the convolutional sparse coding and trust classification system, and then constructs the ...
Xu Zhaosheng
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Bayesian modelling of music : algorithmic advances and experimental studies of shift-invariant sparse coding [PDF]
, 2005 PhDIn order to perform many signal processing tasks such as classification, pattern recognition and coding, it is helpful to specify a signal model in terms of meaningful signal structures.
Blumensath, Thomas
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Image Classification Method Based on Non-negative Elastic Net Sparse Coding Algorithm [PDF]
Jisuanji gongcheng, 2017 In order to improve the image classification accuracy,this paper proposes a Non-negative Elastic Net Sparse Coding(NENSC)algorithm.This algorithm combines the advantages of non-negative sparse coding and elastic net algorithm.It introduces an l2norm ...
ZHANG Yong,ZHANG Yangyang,CHENG Hong,ZHANG Yanxia
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Fast and Efficient Union of Sparse Orthonormal Transforms via DCT and Bayesian Optimization
Applied Sciences, 2022 Sparse orthonormal transform is based on orthogonal sparse coding, which is relatively fast and suitable in image compression such as analytic transforms with better performance.
Gihwan Lee, Yoonsik Choe
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Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images. [PDF]
PLoS Computational Biology, 2013 The sparse coding hypothesis has enjoyed much success in predicting response properties of simple cells in primary visual cortex (V1) based solely on the statistics of natural scenes. In typical sparse coding models, model neuron activities and receptive
Joel Zylberberg, Michael Robert DeWeese
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Efficient sparse coding in early sensory processing: lessons from signal recovery. [PDF]
PLoS Computational Biology, 2012 Sensory representations are not only sparse, but often overcomplete: coding units significantly outnumber the input units. For models of neural coding this overcompleteness poses a computational challenge for shaping the signal processing channels as ...
András Lörincz +2 more
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A Probabilistic Analysis of Sparse Coded Feature Pooling and Its Application for Image Retrieval. [PDF]
PLoS ONE, 2015 Feature coding and pooling as a key component of image retrieval have been widely studied over the past several years. Recently sparse coding with max-pooling is regarded as the state-of-the-art for image classification. However there is no comprehensive
Yunchao Zhang +3 more
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Sparse representations in audio & music: from coding to source separation [PDF]
, 2010 —Sparse representations have proved a powerful toolin the analysis and processing of audio signals and already lieat the heart of popular coding standards such as MP3 andDolby AAC.
Davies, ME +15 more
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Frontiers in Computational Neuroscience, 2019 Sparse coding models of natural images and sounds have been able to predict several response properties of neurons in the visual and auditory systems. While the success of these models suggests that the structure they capture is universal across domains ...
Eric McVoy Dodds +4 more
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Sparse representation based intraframe and semi‐intraframe video coding schemes for low bitrates
IET Image Processing, 2021 This paper proposes some extensions of the successful sparse coding of still images to intraframe and semi‐intraframe video coding. The presented frameworks apply the efficient K‐singular value decomposition and recursive least squares dictionary ...
Maziar Irannejad +1 more
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