Infrared Image Sequence Complexity Analysis Based on Multi-attribute Decision Making

Li-yong, 乔立永 Qiao; Li-xin, 徐立新 Xu; Min, 高敏 Gao

doi:10.3788/gzxb20154403.0311001

Cited by 2 publications

(1 citation statement)

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“…Decision-making is the core of management and has been widely applied in the economy, industry and engineering [1,2,3,4]. It usually depends on administrators’ knowledge and experience but increasingly relies on objective data and information.…”

Section: Introductionmentioning

confidence: 99%

A Novel Group Decision-Making Method Based on Sensor Data and Fuzzy Information

Bai

Zhang

Wang

et al. 2016

Sensors

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Algal bloom is a typical phenomenon of the eutrophication of rivers and lakes and makes the water dirty and smelly. It is a serious threat to water security and public health. Most scholars studying solutions for this pollution have studied the principles of remediation approaches, but few have studied the decision-making and selection of the approaches. Existing research uses simplex decision-making information which is highly subjective and uses little of the data from water quality sensors. To utilize these data and solve the rational decision-making problem, a novel group decision-making method is proposed using the sensor data with fuzzy evaluation information. Firstly, the optimal similarity aggregation model of group opinions is built based on the modified similarity measurement of Vague values. Secondly, the approaches’ ability to improve the water quality indexes is expressed using Vague evaluation methods. Thirdly, the water quality sensor data are analyzed to match the features of the alternative approaches with grey relational degrees. This allows the best remediation approach to be selected to meet the current water status. Finally, the selection model is applied to the remediation of algal bloom in lakes. The results show this method’s rationality and feasibility when using different data from different sources.

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Section: Introductionmentioning

confidence: 99%

A Novel Group Decision-Making Method Based on Sensor Data and Fuzzy Information

Bai

Zhang

Wang

et al. 2016

Sensors

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Evaluation of Image Complexity Based on SVOR

Xiao

Jin

Zhu

et al. 2018

Int. J. Patt. Recogn. Artif. Intell.

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Because of the subjectivity of human beings, the evaluation of image complexity in the Human Vision System (HVS) cannot be provided accurately by traditional image complexity evaluation models. In the 2016 Conference on Computer Vision and Pattern Recognition (CVPR 2016), an evaluation method of visual search difficulty based on the visual search time was proposed for the first time. In this paper, the ordinal relation of the image complexity for human perception was discussed, and a quantitative evaluation model based on Convolutional Neural Network (CNN) features and Support Vector Ordinal Regression (SVOR) with explicit inequality constraints on the thresholds was proposed. The results showed that the evaluation models based on SVOR and pyramid CNN features of images can describe the order relation of image complexity among different images more accurately, which achieve the Kendall’s tau correlation of 0.4858, better than SVR overall under the same condition, whose highest Kendall’s tau correlation is 0.4794.

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Infrared Image Sequence Complexity Analysis Based on Multi-attribute Decision Making

Cited by 2 publications

References 0 publications

A Novel Group Decision-Making Method Based on Sensor Data and Fuzzy Information

A Novel Group Decision-Making Method Based on Sensor Data and Fuzzy Information

Evaluation of Image Complexity Based on SVOR

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