Incremental subspace learning via non-negative matrix factorization

Bucak, Serhat S.; Günsel, Bilge

doi:10.1016/j.patcog.2008.09.002

Cited by 118 publications

(81 citation statements)

References 13 publications

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“…The results for SG, MOG and PCA comes from [27]. The results for the INMF was provided by their authors [17]. The KDE was implemented in Microsoft Visual C++ and the IRT and IMMC was implemented in Matlab.…”

Section: Resultsmentioning

confidence: 99%

“…In the same way, Partial Least Squares (PLS) methods [30] give a nice perspective to model robustly the background. MOG [25] KDE [26] PCA [6] INMF [17] IRT [18] IMMC Table 3. Results on Wallflower dataset [23] …”

Section: Resultsmentioning

confidence: 99%

“…The ICA model was tested on traffic scenes [14] and show robustness in changing background like illumination changes. Recently, Chu et al [16] used a Non-negative Matrix Factorization algorithm to model dynamic backgrounds and Bucak et al [17] preferred an Incremental version of the Non-negative Matrix Factorization (INMF) which presents similar performance than the incremental PCA [8]. In order to take into account the spatial information, Li et al [18] used an Incremntal Rank-(R1,R2,R3) Tensor (IRT).…”

Section: Introductionmentioning

confidence: 99%

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Background Modeling via Incremental Maximum Margin Criterion

Marghes

Bouwmans

2011

Computer Vision – ACCV 2010 Workshops

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Abstract. Subspace learning methods are widely used in background modeling to tackle illumination changes. Their main advantage is that it doesn't need to label data during the training and running phase. Recently, White et al. [1] have shown that a supervised approach can improved significantly the robustness in background modeling. Following this idea, we propose to model the background via a supervised subspace learning called Incremental Maximum Margin Criterion (IMMC). The proposed scheme enables to initialize robustly the background and to update incrementally the eigenvectors and eigenvalues. Experimental results made on the Wallflower datasets show the pertinence of the proposed approach.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Background Modeling via Incremental Maximum Margin Criterion

Marghes

Bouwmans

2011

Computer Vision – ACCV 2010 Workshops

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“…In the same category, Yamazaki et al [30] and Tsai et al [46] have used an Independent Component Analysis (SL-ICA). In another way, Bucak et al [31,47] have proposed an Incremental Non-negative Matrix Factorization (SL-INMF) to reduce the dimension. In order to take into account the spatial information, Li et al [32] have used an Incremental Rank-(R 1 ,R 2 ,R 3 ) Tensor (SL-IRT).…”

Section: Fig (4) Dynamic Backgroundsmentioning

confidence: 99%

“…The improvements consist to enhance the adaptation and the robustness by using incremental and robust PCA algorithms [33][34][35][36][37][38][39][40][41][42][43][44][45]. The variants consist to use an other subspace learning algorithms as the Independent Component Analysis (ICA) [30,46], Incremental Non-negative Matrix Factorization (INMF) [31,47] and Incremental Rank-(R 1 ,R 2 ,R 3 ) Tensor (IRT) [32].…”

Section: Fig (4) Dynamic Backgroundsmentioning

confidence: 99%

Subspace Learning for Background Modeling: A Survey

Bouwmans¹

2010

CSENG

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Background modeling is often used to detect moving object in video acquired by a fixed camera. Recently, subspace learning methods have been used to model the background in the idea to represent online data content while reducing dimension significantly. The first method using Principal Component Analysis (PCA) was proposed by Oliver et al.[1] and a representative patent using PCA concerns the detection of cars and persons in video surveillance [2]. Numerous improvements and variants were developed over the recent years. The purpose of this paper is to provide a survey and an original classification of these improvements. Firstly, we classify the improvements of the PCA in term of strategies and the variants in term of the used subspace learning algorithms. Then, we present a comparative evaluation of the variants and evaluate them with the state-of-art algorithms (SG, MOG, and KDE) by using the Wallflower dataset.

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Incremental Nonnegative Matrix Factorization with Sparseness Constraint for Image Representation

Sun

Wang

et al. 2018

Lecture Notes in Computer Science

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Incremental subspace learning via non-negative matrix factorization

Cited by 118 publications

References 13 publications

Background Modeling via Incremental Maximum Margin Criterion

Background Modeling via Incremental Maximum Margin Criterion

Subspace Learning for Background Modeling: A Survey

Incremental Nonnegative Matrix Factorization with Sparseness Constraint for Image Representation

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