On feature extraction by mutual information maximization

Torkkola, Kari

doi:10.1109/icassp.2002.5743865

Cited by 78 publications

(102 citation statements)

References 4 publications

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“…It performed only slightly better than the SIFT method for the NMI evaluation metric. This is in part due to the distinct feature selection criteria bias towards NMI performance, which stems from the removal criteria calibration (Torkkola, 2003). It had the largest performance increase over all other methods for the RMSE CE evaluation metric.…”

Section: Discussionmentioning

confidence: 99%

“…The mouse boundaries, and are extracted using the marching squares algorithm on the intensity thresholded white light images, once small areas outside the central mouse boundary have been removed (Ho et al , 2005). NMI is a measurement of the mutual information between corresponding images (Wells et al , 1996; Skouson et al , 2001; Pluim et al , 2003; Torkkola, 2003). FSIM evaluates gradient and phase information between corresponding images (Zhang et al , 2011).…”

Section: Methodsmentioning

confidence: 99%

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Canny edge-based deformable image registration

et al. 2017

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This work focuses on developing a 2D Canny edge-based deformable image registration (Canny DIR) algorithm to register in vivo white light images taken at various time points. This method uses a sparse interpolation deformation algorithm to sparsely register regions of the image with strong edge information. A stability criterion is enforced which removes regions of edges that do not deform in a smooth uniform manner. Using a synthetic mouse surface ground truth model, the accuracy of the Canny DIR algorithm was evaluated under axial rotation in the presence of deformation. The accuracy was also tested using fluorescent dye injections, which were then used for gamma analysis to establish a second ground truth. The results indicate that the Canny DIR algorithm performs better than rigid registration, intensity corrected Demons, and Distinctive Features for all evaluation matrices and ground truth scenarios. In conclusion Canny DIR performs well in the presence of the unique lighting and shading variations associated with white-light-based image registration.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Canny edge-based deformable image registration

et al. 2017

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“…Because feature selection occurs outside the bounds of a particular model, quantitative measures need to be put in place to evaluate the set such as the mutual information measure employed by Torkkola [23] or feature value distances between the target class and near-hit/near-miss examples employed by the Relief algorithm [12]. …”

Section: Related Workmentioning

confidence: 99%

Sensor selection to support practical use of health‐monitoring smart environments

Cook

Holder

2011

WIREs Data Min & Knowl

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The data mining and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track activities that people normally perform as part of their daily routines. One question that frequently arises, however, is how many smart home sensors are needed and where should they be placed in order to accurately recognize activities? We employ data mining techniques to look at the problem of sensor selection for activity recognition in smart homes. We analyze the results based on six data sets collected in five distinct smart home environments.

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“…Other sorts of non-parametric methods include [7,11], which calculate their error rate based criteria by the non-parametrically estimated distribution other than by the supposed normal distribution; and [4,10], which directly define the criteria according to the decision boundary. Here, we do not discuss them in detail, but only focus on the neighbor based methods.…”

Section: Introductionmentioning

confidence: 99%

Parzen Discriminant Analysis

Fang

Shan²,

Chang³

et al. 2008

2008 19th International Conference on Pattern Recognition

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In this paper, we propose a non-parametric Discriminant Analysis method (no assumption on the distributions of classes), called Parzen Discriminant Analysis (PDA). Through a deep investigation on the non-parametric density estimation, we find that minimizing/maximizing the distances between each data sample and its nearby similar/dissimilar samples is equivalent to minimizing an upper bound of the Bayesian error rate. Based on this theoretical analysis, we define our criterion as maximizing the average local dissimilarity scatter with respect to a fixed average local similarity scatter. All local scatters are calculated in fixed size local regions, resembling the idea of Parzen estimation. Experiments in UCI machine learning database show that our method impressively outperforms other related neighbor based non-parametric methods.

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On feature extraction by mutual information maximization

Cited by 78 publications

References 4 publications

Canny edge-based deformable image registration

Canny edge-based deformable image registration

Sensor selection to support practical use of health‐monitoring smart environments

Parzen Discriminant Analysis

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