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Journal of Flow Visualization and Image Processing

Publicado 4 números por año

ISSN Imprimir: 1065-3090

ISSN En Línea: 1940-4336

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 0.6 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.6 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00013 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.14 SJR: 0.201 SNIP: 0.313 CiteScore™:: 1.2 H-Index: 13

Indexed in

BLIND FEATURE SELECTION AND EXTRACTION IN A 3D IMAGE CUBE

Volumen 19, Edición 2, 2012, pp. 97-111
DOI: 10.1615/JFlowVisImageProc.2012005150
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SINOPSIS

As the number of spectral hands of high spectral resolution increases, the ability to detect more detailed classes should also increase, and the classification accuracy should increase as well. As the number of spectral bands becomes large, the limitation on performance imposed by the limited number of observed samples can become severe. A number of techniques for specific feature selection and extraction have been developed to reduce the dimensionality and target detection without the loss of class reparability. This suggests the need for reducing the dimensionality via a preprocessing method that takes into consideration the high dimensional feature space properties. Such reduction should enable the estimation of the feature extraction parameters to be more accurate bu using the technique referred to as the maximum conditional log likelihood estimation. This technique is able to bypass many of the problems of the limitation of small/large numbers of observed samples by making the computations in a lower dimensional space, and optimizing the function called the expectation and maximization. This method leads also to a high dimensional version of the feature selection and extraction algorithm, which requires significantly less computation than the normal procedure. A set of tests with real data evaluates the performance and illustrates the effectiveness of the proposed method. The entire work is done by using MATLAB.

CITADO POR
  1. Ahmad Muhammad, Khan Adil Mehmood, Hussain Rasheed, Graph‐based spatial–spectral feature learning for hyperspectral image classification, IET Image Processing, 11, 12, 2017. Crossref

  2. Ahmad Muhammad, Khan Adil Mehmood, Hussain Rasheed, Protasov Stanislav, Chow Francis, Khattak Asad Masood, Unsupervised geometrical feature learning from hyperspectral data, 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016. Crossref

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