A goal oriented feature selection for collaborative GIS

Diamantini, Claudia; Gemelli, Alberto; Potena, Domenico

doi:10.1109/cts.2009.5067458

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…If it is differentiated over the features, the cost transforms the rank model in a cost function. Generally, the output of this algorithm is the Rank Model [4] (RM), defined as follows:…”

Section: A Proposed Solutionmentioning

confidence: 99%

A novel feature ranking modelling in GIS context: Addressing complexity and cost issues

Gemelli

Diamantini

Potena

2009

2009 17th International Conference on Geoinformatics

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nowadays, there is the trend to carry out decisions and analysis on geospatial data by a massive computational approach. The amount of geospatial information available is increasing exponentially as result of the increasing interoperability between informative systems. In a multiplicity of applications and services spatial decision is carried out to pursue business goals, often without involving experts in geography. The informative systems have an increasing autonomous decisional capability on information selection and analysis. The demand is to have systems that require only an input goal, and produces decisions that humans can understand and integrate with their own decisions. In this paper it is proposed an automatic method of feature ranking, which can sort a heterogeneous set of features by their importance in accomplishing an analytical goal. This method produces a rank model that helps to select the minimal set of features needed to pursue a goal with a wanted accuracy or resources involvement. This feature ranking is expected to supports fundamental decisional making in elaborating geospatial data. The method is based on data mining algorithms; the obtained rank model appears to be spatially scalable and fits well to human form of knowledge.

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“…If it is differentiated over the features, the cost transforms the rank model in a cost function. Generally, the output of this algorithm is the Rank Model [4] (RM), defined as follows:…”

Section: A Proposed Solutionmentioning

confidence: 99%

A novel feature ranking modelling in GIS context: Addressing complexity and cost issues

Gemelli

Diamantini

Potena

2009

2009 17th International Conference on Geoinformatics

View full text Add to dashboard Cite

show abstract

“…Interpolation based methods exhibit low computational cost, but with limited restoration performance. Bayes theorem [34] and linear regression [39] have been utilized in video super-resolution. Researchers pay their interest on the example-based single-image SR, in which the external and/or internal exemplars are sheathed for learning the mappings using the low-resolution patch to yield the high-resolution patches.…”

Section: Introductionmentioning

confidence: 99%

Hybrid SVR-ACNN model: Proposed Video Super-Resolution method for video enhancement

Reddy¹,

Udayarani²

2019

Journal of Digital Information Management

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Video super-resolution techniques are the need of the hour for the high-resolution display devices as the current high-resolution videos are the basic question. Even though there are a large number of techniques employed for the video super-resolution, all these existing techniques face a hectic challenge at various conditions. Thus, this paper proposes an effective video resolution strategy using the hybrid Support vector regression-Actor Critic Neural Network (SVR-ACNN) model for video enhancement. The super-resolution images formed using the individual SVR model and Actor Critic Neural Network are integrated using the weighted average concept. The Actor Critic Neural Network is tuned optimally using the proposed Fractional-based Sine Cosine algorithm (F-SCA) that is responsible for the global optimal convergence. The experimentation of the proposed method utilizes three videos taken from the Cambridgedriving Labeled Video Database (CamVid), and the results are analyzed for three scaling factors. The outcome of the analysis proves that the proposed method offers a better super-resolution image with a better PSNR, SSIM, and SDME of 33.6447dB, 0.9398, and 45.2779, respectively.

show abstract

A goal oriented feature selection for collaborative GIS

Cited by 2 publications

References 13 publications

A novel feature ranking modelling in GIS context: Addressing complexity and cost issues

A novel feature ranking modelling in GIS context: Addressing complexity and cost issues

Hybrid SVR-ACNN model: Proposed Video Super-Resolution method for video enhancement

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