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Online Robust Dictionary Learning

Cewu Lu, Jiaping Shi, Jiaya Jia
2013 2013 IEEE Conference on Computer Vision and Pattern Recognition  
Online dictionary learning is particularly useful for processing large-scale and dynamic data in computer vision.  ...  In this paper, we propose a new online framework enabling the use of ℓ 1 sparse data fitting term in robust dictionary learning, notably enhancing the usability and practicality of this important technique  ...  A few learned dictionary atoms using our Online Robust Dictionary Learning (ORDL).  ... 
doi:10.1109/cvpr.2013.60 dblp:conf/cvpr/LuSJ13 fatcat:tobhfe2d2zdfzbslkefqyjz56e

Online Robust Non-negative Dictionary Learning for Visual Tracking

Naiyan Wang, Jingdong Wang, Dit-Yan Yeung
2013 2013 IEEE International Conference on Computer Vision  
In particular, we propose an online robust non-negative dictionary learning algorithm for updating the object templates so that each learned template can capture a distinctive aspect of the tracked object  ...  This paper studies the visual tracking problem in video sequences and presents a novel robust sparse tracker under the particle filter framework.  ...  In this paper, we present an online robust non-negative dictionary learning algorithm for updating the object templates. The learned templates for two video sequences are shown in Fig. 1 .  ... 
doi:10.1109/iccv.2013.87 dblp:conf/iccv/WangWY13 fatcat:kcrf5dsbvzh4fkftwy3e3jkz6i

Robust Object Tracking with Online Multi-lifespan Dictionary Learning

Junliang Xing, Jin Gao, Bing Li, Weiming Hu, Shuicheng Yan
2013 2013 IEEE International Conference on Computer Vision  
We propose to perform template updating, in a new perspective, as an online incremental dictionary learning problem, which is efficiently solved through an online optimization procedure.  ...  We derive effective observation models both generatively and discriminatively based on the online multi-lifespan dictionary learning model and deploy them to the Bayesian sequential estimation framework  ...  examples of the three lifespan dictionaries learned using the online dictionary learning algorithm.  ... 
doi:10.1109/iccv.2013.88 dblp:conf/iccv/XingGLHY13 fatcat:h2o6y2cb7fa7vc6ihekvovb4ti

Online Learning Discriminative Dictionary with Label Information for Robust Object Tracking

Baojie Fan, Yingkui Du, Yang Cong
2014 Abstract and Applied Analysis  
Label information from training data is incorporated into the dictionary learning process to construct a robust and discriminative dictionary.  ...  A supervised approach to online-learn a structured sparse and discriminative representation for object tracking is presented.  ...  We construct a robust object function for online dictionary learning and the optimal classifier.  ... 
doi:10.1155/2014/189317 fatcat:ri26ch4h5jbctn55lymnyyhl6a

Online Multi-Modal Robust Non-Negative Dictionary Learning for Visual Tracking

Xiang Zhang, Naiyang Guan, Dacheng Tao, Xiaogang Qiu, Zhigang Luo, Wen-Bo Du
2015 PLoS ONE  
In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency.  ...  To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing  ...  an online multi-modal robust non-negative dictionary learning (OMRNDL) method.  ... 
doi:10.1371/journal.pone.0124685 pmid:25961715 pmcid:PMC4427315 fatcat:foao36zxbnefhmjt2kcirts2vy

A Novel Hyperspectral Endmember Extraction Algorithm Based on Online Robust Dictionary Learning

Xiaorui Song, Lingda Wu
2019 Remote Sensing  
To solve this problem, this paper proposes a novel endmember extraction approach based on online robust dictionary learning, termed EEORDL.  ...  However, current endmember extraction methods based on dictionary learning are not robust enough in noisy environments.  ...  Endmember Extraction Based on Online Robust Dictionary Learning In real situations, the HSI data often contain noise and outliers.  ... 
doi:10.3390/rs11151792 fatcat:ednfb2fhfre37bqc3uwmgcb7ve

Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning

Ruogu Fang, Tsuhan Chen, Pina C. Sanelli
2013 Medical Image Analysis  
We first build a dictionary from high-dose perfusion maps using online dictionary learning and then perform deconvolution-based hemodynamic parameters estimation on the low-dose CTP data.  ...  In this paper, we propose a robust sparse perfusion deconvolution method (SPD) to estimate cerebral blood flow in CTP performed at low radiation dose.  ...  Online dictionary learning solves a relaxed l 1 norm problem which is convex and therefore results in more robust dictionary. This leads to the differences in the two learned dictionaries.  ... 
doi:10.1016/ pmid:23542422 pmcid:PMC4196260 fatcat:hckaesh3lzaevobibxdiobtkwq

A Method for Robust Online Classification using Dictionary Learning: Development and Assessment for Monitoring Manual Material Handling Activities Using Wearable Sensors [article]

Babak Barazandeh, Mohammadhussein Rafieisakhaei, Sunwook Kim, Zhenyu Kong, Maury A. Nussbaum
2018 arXiv   pre-print
This procedure is based on the idea of dictionary learning (DL): we optimize the design matrix formed by training datasets to minimize both redundancy and coherency as well as reducing the size of these  ...  Our proposed method optimizes the design matrix (aka dictionary) in the linear model used for SRC, minimizing its ill-posedness to achieve a sparse solution.  ...  Note to Practitioners-This paper develops a fast and robust classification method for online sensor data classification based on the dictionary learning principle.  ... 
arXiv:1810.09447v1 fatcat:dithzr7wcveftpxmyxdg6xjivm

Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning

Xiaorui Song, Lingda Wu
2019 Applied Sciences  
We subsequently proposed a new algorithm for constructing a spectral dictionary directly from hyperspectral data by introducing an online learning optimization method and performing dictionary learning  ...  using a robust function.  ...  Conclusions In this paper, we proposed an HSI inpainting algorithm based on online robust spectral dictionary learning.  ... 
doi:10.3390/app9153062 fatcat:p2nkz5gh7vdtvke2rgsge67j4i

Online non-negative discriminative dictionary learning for tracking

Weisong Wang, Fei Yang, Hongzhi Zhang
2019 EURASIP Journal on Advances in Signal Processing  
In this paper, online non-negative discriminative dictionary learning for tracking is proposed, which combines the advantages of the global dictionary learning model and the class-specific dictionary learning  ...  In order to improve the classification ability of dictionaries, the class correlation was proposed to guide the learning of discriminant dictionaries, which makes full use of the correlation and difference  ...  To this end, we explore online dictionary learning tracking algorithm and introduce the online discriminant dictionary learning tracking strategy.  ... 
doi:10.1186/s13634-019-0638-0 fatcat:modyw3izyzbxtijoxo7fqmax2m

Online Learning a High-Quality Dictionary and Classifier Jointly for Multitask Object Tracking

Baojie Fan, Hao Gao, Yang Cong, Yingkui Du, Yangdong Tang
2014 IEEE Multimedia  
To survey many of these algorithms, we refer the reader to earlier work. [1] [2] [3] [4] In this article, we present a supervised approach to online learning and update a structured sparse and discriminative  ...  We also jointly train a high-quality dictionary and optimal linear classifier. All training samples from the object and background are simultaneously involved in the dictionary learning process.  ...  Possible future work for our method includes online and robust discriminative dictionary learning and structured low-rank representations for real-time object tracking.  ... 
doi:10.1109/mmul.2014.53 fatcat:7zgen63rg5hkjm4mmeyxuylame

Learning Local Appearances With Sparse Representation for Robust and Fast Visual Tracking

Tianxiang Bai, You-Fu Li, Xiaolong Zhou
2015 IEEE Transactions on Cybernetics  
In this paper, we present a novel appearance model using sparse representation and online dictionary learning techniques for visual tracking.  ...  In our approach, the visual appearance is represented by sparse representation, and the online dictionary learning strategy is used to adapt the appearance variations during tracking.  ...  The online learned sparse dictionary is robust to the occlusions because it models the target appearance with local features.  ... 
doi:10.1109/tcyb.2014.2332279 pmid:25029548 fatcat:23bi47bzzbdeho6v4zyj7bynsm

Domain Adaptation Image Classification Based on Multi-sparse Representation

2017 KSII Transactions on Internet and Information Systems  
And each intermediate subspace is modeled through online dictionary learning with target data updating.  ...  An augmented feature representation produced by invariant sparse codes across the source, intermediate and target domain dictionaries is employed for across domain recognition.  ...  One of the effective ways to resolve the problem is to update the previous dictionary with mini-batch samples instead of a single sample, as shown in online dictionary learning [27] and online robust  ... 
doi:10.3837/tiis.2017.05.016 fatcat:yxjr3qjjavgwpl6cnbqdef5qsm

Universal Hough dictionaries for object tracking

Fausto Milletari, Wadim Kehl, Federico Tombari, Slobodan Ilic, Seyed-Ahmad Ahmadi, Nassir Navab
2015 Procedings of the British Machine Vision Conference 2015  
We propose a novel approach to online visual tracking that combines the robustness of sparse coding with the flexibility of voting based methods.  ...  During online tracking we adapt the generic knowledge learned by the dictionary to the specific object being tracked, by associating a set of votes and local object appearances to each atom.  ...  Dictionary learning. Signal Processing Magazine, IEEE, 28(2):27-38, 2011. [2] Yi Wu, Jongwoo Lim, and Ming-Hsuan Yang. Online object tracking: A benchmark.  ... 
doi:10.5244/c.29.122 dblp:conf/bmvc/MilletariKTIAN15 fatcat:hqcf2pkvgbbl3kg2vi5dnau56m

Robust Dictionary Learning by Error Source Decomposition

Zhuoyuan Chen, Ying Wu
2013 2013 IEEE International Conference on Computer Vision  
Experiments on synthetic data as well as real applications have shown satisfactory performance of this new robust dictionary learning approach.  ...  In contrast to most existing methods that learn the dictionary from clean data, this paper is targeted at handling corruptions and outliers in training data for dictionary learning.  ...  This paper provides a coordinate descent solution for robust dictionary learning, an online acceleration method, and its convergence property.  ... 
doi:10.1109/iccv.2013.276 dblp:conf/iccv/ChenW13 fatcat:g6e7uu6kh5dddhn47jghimsdl4
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