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Multiscale sequence modeling with a learned dictionary [article]

Bart van Merriënboer, Amartya Sanyal, Hugo Larochelle, Yoshua Bengio
2017 arXiv   pre-print
A variation of the byte-pair encoding (BPE) compression algorithm is used to learn the dictionary of tokens that the model is trained with.  ...  We propose a generalization of neural network sequence models.  ...  Another consideration would be to learn the dictionary and the sequence model jointly.  ... 
arXiv:1707.00762v2 fatcat:qgwajxvtv5bpzo3zqylpjbthei

A Dynamical Appearance Model Based on Multiscale Sparse Representation: Segmentation of the Left Ventricle from 4D Echocardiography [chapter]

Xiaojie Huang, Donald P. Dione, Colin B. Compas, Xenophon Papademetris, Ben A. Lin, Albert J. Sinusas, James S. Duncan
2012 Lecture Notes in Computer Science  
It employs multiscale sparse representation of local appearance, learns online multiscale appearance dictionaries as the image sequence is segmented sequentially, and integrates a spectrum of complementary  ...  While learning offline dynamical priors from databases has received considerable attention, these priors may not be suitable for post-infarct patients and children with congenital heart disease.  ...  Online Multiscale Dictionary Learning To obtain the discriminant R t , {D 1 t , D 2 t } k and β k need to be learned.  ... 
doi:10.1007/978-3-642-33454-2_8 fatcat:75uvvbk2r5hlrg4cdavcbcn7le

Transferring Visual Prior for Online Object Tracking

Qing Wang, Feng Chen, Jimei Yang, Wenli Xu, Ming-Hsuan Yang
2012 IEEE Transactions on Image Processing  
From a collection of realworld images, we learn an overcomplete dictionary to represent visual prior.  ...  Experiments on a variety of challenging sequences with comparisons to several state-ofthe-art methods demonstrate that more robust object tracking can be achieved by transferring visual prior.  ...  Wang was a visiting student at the University of California at Merced.  ... 
doi:10.1109/tip.2012.2190085 pmid:22491081 fatcat:obwthxkxqvag5eo6rwtqsaqpp4

Learning a collaborative multiscale dictionary based on robust empirical mode decomposition [article]

Rui Chen, Huizhu Jia, Xiaodong Xie, Wen Gao
2017 arXiv   pre-print
In order to further enhance sparsity and generalization, a tolerance dictionary is learned using a coherence regularized model. A fast proximal scheme is developed to optimize this model.  ...  Due to combining the advantages of generic multiscale representations with learning based adaptivity, multiscale dictionary representation approaches have the power in capturing structural characteristics  ...  [21] used a multiscale quadtree model to decompose big image patch along the tree to patches of small scales, and then a dictionary is learned at each scale.  ... 
arXiv:1704.04422v1 fatcat:jnwujvujgzgzbdqzsx2irqqvpe

Learning Multiscale Sparse Representations for Image and Video Restoration

Julien Mairal, Guillermo Sapiro, Michael Elad
2008 Multiscale Modeling & simulation  
A framework for learning multiscale sparse representations of color images and video with overcomplete dictionaries is presented in this paper.  ...  matching pursuit and SVD, this proposed multiscale learned representation is obtained based on an efficient quadtree decomposition of the learned dictionary and overlapping image patches.  ...  The task of learning a multiscale dictionary has been addressed in [32] in the general context of sparsifying image content.  ... 
doi:10.1137/070697653 fatcat:dt7jiwgz65f3xlzv6aawcnz3bi

Multiscale Sparse Image Representationwith Learned Dictionaries

Julien Mairal, Guillermo Sapiro, Michael Elad
2007 2007 IEEE International Conference on Image Processing  
This paper introduces a new framework for learning multiscale sparse representations of natural images with overcomplete dictionaries.  ...  We show that these are further improved with a multiscale approach, based on a Quadtree decomposition.  ...  The task of learning a multiscale dictionary has been addressed in [8] in the general context of sparsifying image content.  ... 
doi:10.1109/icip.2007.4379257 dblp:conf/icip/MairalSE07 fatcat:s3kyezd7azag3iohj3dc6pwmtu

Metric Learning Based Structural Appearance Model for Robust Visual Tracking

Yuwei Wu, Bo Ma, Min Yang, Jian Zhang, Yunde Jia
2014 IEEE transactions on circuits and systems for video technology (Print)  
Index Terms-Appearance modeling, multiple instance metric learning, multiscale max pooling, object tracking, sparse coding.  ...  Appearance modeling is a key issue for the success of a visual tracker. Sparse representation based appearance modeling has received an increasing amount of interest in recent years.  ...  However, straightforward dictionary updating with newly obtained results is prone to drifting because of the accumulation of errors. 2) The measurement of reconstruction errors usually employs a predefined  ... 
doi:10.1109/tcsvt.2013.2291283 fatcat:y4y552vhbbcz7j22355ythgs6u

Sparsity based denoising of spectral domain optical coherence tomography images

Leyuan Fang, Shutao Li, Qing Nie, Joseph A. Izatt, Cynthia A. Toth, Sina Farsiu
2012 Biomedical Optics Express  
We learn a sparse representation dictionary for each of these high-SNR images, and utilize such dictionaries to denoise the low-SNR B-scans.  ...  In this paper, we make contact with the field of compressive sensing and present a development and generalization of tools and results for reconstructing irregularly sampled tomographic data.  ...  We learned a sparse representation dictionary for each of these high-SNR images, and utilized them to denoise the low-SNR B-scans.  ... 
doi:10.1364/boe.3.000927 pmid:22567586 pmcid:PMC3342198 fatcat:2u7lupvmxnarnoyfbypg7jinba

Learning real and complex overcomplete representations from the statistics of natural images

Bruno A. Olshausen, Charles F. Cadieu, David K. Warland, Vivek K. Goyal, Manos Papadakis, Dimitri Van De Ville
2009 Wavelets XIII  
We show how an overcomplete dictionary may be adapted to the statistics of natural images so as to provide a sparse representation of image content.  ...  We demonstrate this point by showing that it is possible to learn the higher-order structure of dynamic phase-i.e., motion-from the statistics of natural image sequences.  ...  ACKNOWLEDGMENTS The authors wish to thank Fritz Sommer and Martin Rehn for early collaborative efforts 13 and helpful discussions characterizing the diversity of the learned basis functions.  ... 
doi:10.1117/12.825882 fatcat:xsss6ur6trazdllw5hj3yi3bxa

Improved Image Fusion Method Based on Sparse Decomposition

Xiaomei Qin, Yuxi Ban, Peng Wu, Bo Yang, Shan Liu, Lirong Yin, Mingzhe Liu, Wenfeng Zheng
2022 Electronics  
It also solves the problem that dictionary training sparse approximation takes a long time.  ...  In the principle of lens imaging, when we project a three-dimensional object onto a photosensitive element through a convex lens, the point intersecting the focal plane can show a clear image of the photosensitive  ...  One PC with I5-6500CPU/GTX1060ti/16G/256GSSD; 2. A ByslorPylon industrial camera, model: acA640-120uc; 3.  ... 
doi:10.3390/electronics11152321 fatcat:tc3wmtw6b5ec3azm3ldtuozaqu

3-D OCT data denoising with nonseparable oversampled lapped transform

Shogo Muramatsu, Samuel Choi, Takumi Kawamura
2015 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)  
It is possible to apply a dictionary learning technique to the design by preparing examples. NSOLT is capable of having rational redundancy by controlling the number of channels and decimation ratio.  ...  In this study, a denoising technique is proposed by combining learned NSOLT dictionary and iterative hard thresholding (IHT), and the performance of the proposed method is evaluated for 3-D OCT data.  ...  Under a structural constraint of the Parseval tight frame property, we can also design an NSOLT dictionary with multiscale representation through a dictionary learning approach [12] , [15] .  ... 
doi:10.1109/apsipa.2015.7415402 dblp:conf/apsipa/MuramatsuCK15 fatcat:4tdxbpkekzc6tngjhqfbzhgfsa

Waveform speech coding using multiscale recurrent patterns

Frederico S. Pinage, Lara C. R. L. Feio, Eduardo A. B. da Silva, Sergio L. Netto
2010 Proceedings of 2010 IEEE International Symposium on Circuits and Systems  
The so-called MMP (Multidimensional Multiscale Parser) algorithm uses a dictionary which is constantly updated with expansions, contractions, and concatenations of previously encoded segments.  ...  This provides a learning ability to the MMP, particularly suited for coding voiced and silent segments of speech.  ...  By doing so, the MMP algorithm ends up modeling the process in a nonparametric manner through its dictionary content.  ... 
doi:10.1109/iscas.2010.5537982 dblp:conf/iscas/PinageFSN10 fatcat:qzq2i2kppnfp7iuq45lczmrmzy

Sparse Data–Driven Learning for Effective and Efficient Biomedical Image Segmentation

John A. Onofrey, Lawrence H. Staib, Xiaojie Huang, Fan Zhang, Xenophon Papademetris, Dimitris Metaxas, Daniel Rueckert, James S. Duncan
2020 Annual Review of Biomedical Engineering  
We present a selection of techniques that incorporate sparsity, including strategies based on dictionary learning and deep learning, that are aimed at medical image segmentation and related quantification  ...  Sparsity is a powerful concept to exploit for high-dimensional machine learning and associated representational and computational efficiency. Sparsity is well suited for medical image segmentation.  ...  The contour tracker uses multiscale sparse representation of local image appearance and learns online multiscale appearance dictionaries in a boosting framework as the image sequence is segmented frame  ... 
doi:10.1146/annurev-bioeng-060418-052147 pmid:32169002 pmcid:PMC9351438 fatcat:lidlph7avzemtejacndysmhgu4

A fast multiscale framework for data in high-dimensions: Measure estimation, anomaly detection, and compressive measurements

Guangliang Chen, Mark Iwen, Sang Chin, Mauro Maggioni
2012 2012 Visual Communications and Image Processing  
Data sets are often modeled as samples from some probability distribution lying in a very high dimensional space.  ...  In this paper we introduce a novel multiscale density estimator for high dimensional data and apply it to the problem of detecting changes in the distribution of dynamic data, or in a time series of data  ...  With the multiscale GMRA dictionary and density estimates learned on a training data set X n , for example X n = X (0) n , this may be performed as follows.  ... 
doi:10.1109/vcip.2012.6410789 dblp:conf/vcip/ChenICM12 fatcat:uo5aoeb64vdffpvdmpae2mf2le

Dynamic Upsampling of Smoke through Dictionary-based Learning [article]

Kai Bai, Wei Li, Mathieu Desbrun, Xiaopei Liu
2019 arXiv   pre-print
We propose a novel dictionary-based neural network which learns both a fast evaluation of sparse patch encoding and a dictionary of corresponding coarse and fine patches from a sequence of example simulations  ...  In this paper, we propose a novel dictionary-based learning approach to the dynamic upsampling of smoke flows.  ...  We propose a novel dictionary-based neural network which learns both a fast evaluation of sparse patch encoding and a dictionary of corresponding coarse and fine patches from a sequence of example simulations  ... 
arXiv:1910.09166v1 fatcat:mizrec5opbhw5p42b3pntnxllu
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