A comparison of colour texture attributes selected by statistical feature selection and neural network methods

Messer, Kieron; Kittler, Josef

doi:10.1016/s0167-8655(97)00089-5

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…Then, following the method of Messer and Kittler, 76 for the d th dimension ( d = 1,2, …, 56) in the feature space, we define its contribution as follows:

C o n t r i b u t i o n_{d} = {‖ w_{d} ‖}^{2}

where

w_{d}

is the d th dimension of the weight vector w .…”

Section: Methodsmentioning

confidence: 99%

“…These results suggest that the proposed scoring model can recognize the strong therapeutic power of these best therapeutic songs and differentiate them from unlabeled songs. Then, following the method of Messer and Kittler, 76 for the dth dimension (d = 1,2, …, 56) in the feature space, we define its contribution as follows:…”

Section: Experiments 2: Feature Analysismentioning

confidence: 99%

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Finding therapeutic music for anxiety using scoring model

Chen

Guan

et al. 2021

Int J Intell Syst

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A large number of people suffer from anxiety in modern society. As an effective treatment with few side effects, music therapy has been used to reduce anxiety for decades in clinical practice. Yet therapists continue to perform music selection, a key step in music therapy, manually. Considering the growing need for music therapy services and social distancing amid public emergencies, an automatic method for music selection would be of great practical utility. This paper marks the first effort to identify music with therapeutic effects on anxiety reduction via a novel music scoring model. We formulate the calculation of a therapeutic score as a quadratic programming problem, which minimizes score variance among known therapeutic songs while maintaining their superiority over other songs. The proposed model can uncover common features that contribute to anxiety reduction by learning from small and unbalanced data. Using a music therapy experiment, we find that the proposed model outperforms existing techniques in predicting therapeutic songs. Feature analysis is also conducted, revealing that high‐frequency spectrums are important in therapeutic scoring.

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“…Then, following the method of Messer and Kittler, 76 for the d th dimension ( d = 1,2, …, 56) in the feature space, we define its contribution as follows:

C o n t r i b u t i o n_{d} = {‖ w_{d} ‖}^{2}

where

w_{d}

is the d th dimension of the weight vector w .…”

Section: Methodsmentioning

confidence: 99%

Section: Experiments 2: Feature Analysismentioning

confidence: 99%

Finding therapeutic music for anxiety using scoring model

Chen

Guan

et al. 2021

Int J Intell Syst

View full text Add to dashboard Cite

show abstract

“…[1][2][3][4][5][6][7][8] For example, values between nodes may communicate if the nodes are connected. When a node or a connection is removed, it is analogous to ignoring the values processed from a node.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Selecting Interval Values of Input Variables in Connectionist Networks

Chiu

Buczynski

2004

Int. J. Comp. Intel. Appl.

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Selecting parameters can be a powerful mechanism in constructing new evolving connectionist network. However, if a parameter contains partial information such that only some of the values are relevant and others are not, then a selection of the subset of relevant values is more appropriate. Considering the possible values of a parameter of a processing connectionist network as the outcomes of a variable, this research focuses on selecting interval values of the variable. It also considers the partitioning schemes used in generating the intervals from the outcomes of a variable. The goal of this work is to explore variable value selection and its effect in an evolving connectionist network. Using input variables in a backpropagation network, the proposed method evaluates its effect based on training of a dataset, and eliminates those intervals of the variable values that contribute negatively when processed by the network. When a value falls into an interval that has been selected and ignored, it is analogous to a network without processing the corresponding variable, and vice versa. Two approaches for interval partitioning are considered, based on equal-probability (or maximum entropy) and equal-width partitioning scheme. Comparing the best performing network with selection and the one without selection, the experimental results show that the best network with selection can produce better performance accuracy and smaller network size.

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Image Analysis and Computer Vision: 1995

Rosenfeld

1996

Computer Vision and Image Understanding

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A comparison of colour texture attributes selected by statistical feature selection and neural network methods

Cited by 9 publications

References 9 publications

Finding therapeutic music for anxiety using scoring model

Finding therapeutic music for anxiety using scoring model

Evaluation of Selecting Interval Values of Input Variables in Connectionist Networks

Image Analysis and Computer Vision: 1995

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