EM-Based Mixture Models Applied to Video Event Detection

Coelho, Alessandra Martins; Estrela, Vania V.

doi:10.5772/38129

Cited by 15 publications

(2 citation statements)

References 44 publications

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“…The prevailing OF implementations code a n d t e s t a l g o r i t h m s o n g e n e ra l -p u r p o s e computers 11,12,16,39 . The first inspiration is the widespread availability of such hardware and the possibility of reusing code to low requirements concerning software design experience.…”

Section: R E a S O N T O I M P Rov E M O T I O N E S T I M A T I O N Algorithmsmentioning

confidence: 99%

Optical Flow Estimation Using Total Least Squares Variants

Jesús

Estrela

2017

Orient. J. Chem

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The problem of recursively approximating motion resulting from the Optical Flow (OF) in video thru Total Least Squares (TLS) techniques is addressed. TLS method solves an inconsistent system Gu=z , with G and z in error due to temporal/spatial derivatives, and nonlinearity, while the Ordinary Least Squares (OLS) model has noise only in z. Sources of difficulty involve the non-stationarity of the field, the ill-posedness, and the existence of noise in the data. Three ways of applying the TLS with different noise conjectures to the end problem are observed. First, the classical TLS (cTLS) is introduced, where the entries of the error matrices of each row of the augmented matrix [G;z] have zero mean and the same standard deviation. Next, the Generalized Total Least Squares (GTLS) is defined to provide a more stable solution, but it still has some problems. The Generalized Scaled TLS (GSTLS) has G and z tainted by different sources of additive zero-mean Gaussian noise and scaling [G;z] by nonsingular D and E, that is, D[G;z] E makes the errors iid with zero mean and a diagonal covariance matrix. The scaling is computed from some knowledge on the error distribution to improve the GTLS estimate. For moderate levels of additive noise, GSTLS outperforms the OLS, and the GTLS approaches. Although any TLS variant requires more computations than the OLS, it is still applicable with proper scaling of the data matrix.

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Section: R E a S O N T O I M P Rov E M O T I O N E S T I M A T I O N Algorithmsmentioning

confidence: 99%

Optical Flow Estimation Using Total Least Squares Variants

Jesús

Estrela

2017

Orient. J. Chem

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“…Nonlinear DR techniques do not rely on the linearity assumption which leads to more complex embeddings of the data in the high-dimensional space. Performance investigations in medical image retrieval tasks have been done on artificial and natural data capitalizing on the findings from image processing (Pirolla et al,2012;Verbeek, 2006;Wang et al, 2005), artificial intelligence (Bengio & LeCun, 2007), data mining ( (Lee & Verleysen, 2005;Venkatarajan & Braun, 2004;Verbeek, 2006;Wang et al, 2005) and multimedia (Coelho & Estrela, 2012;Coelho & Estrela, 2013. Zhuo et al, 2014.…”

Section: Solutions and Recommendationsmentioning

confidence: 99%