Aviso: para depositar documentos, por favor, inicia sesión e identifícate con tu cuenta de correo institucional de la UCM con el botón MI CUENTA UCM. No emplees la opción AUTENTICACIÓN CON CONTRASEÑA
 

Determining the accuracy in image supervised classification problems

dc.book.titleEUSFLAT-LFA 2011 European Society for Fuzzy Logic and Technology
dc.contributor.authorGómez González, Daniel
dc.contributor.authorMontero De Juan, Francisco Javier
dc.date.accessioned2023-06-20T05:46:33Z
dc.date.available2023-06-20T05:46:33Z
dc.date.issued2011
dc.description.abstractA large number of accuracy measures for crisp supervised classification have been developed in supervised image classification literature. Overall accuracy, Kappa index, Kappa location, Kappa histo and user accuracy are some well-known examples. In this work, we will extend and analyze some of these measures in a fuzzy framework to be able to measure the goodness of a given classifier in a supervised fuzzy classification system with fuzzy reference data. In addition with this, the measures here defined also take into account the preferences of the decision maker in order to differentiate some errors that must not be considered equal in the classification process.en
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/28730
dc.identifier.citationGomez, D., Montero, J.: Determining the accuracy in image supervised classification problems. En: Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011). Atlantis Press, France (2011)
dc.identifier.doi10.2991/eusflat.2011.103
dc.identifier.officialurlhttps//doi.org/10.2991/eusflat.2011.103
dc.identifier.relatedurlhttp://www.atlantis-press.com/php/download_paper.php?id=2288
dc.identifier.urihttps://hdl.handle.net/20.500.14352/45543
dc.issue.number1
dc.language.isoeng
dc.page.final349
dc.page.initial342
dc.publication.placeParis
dc.publisherAtlantis Press
dc.relation.ispartofseriesAdvances in Intelligent Systems Research
dc.rights.accessRightsopen access
dc.subject.cdu519.8
dc.subject.keywordFuzzy image classification
dc.subject.keywordAccuracy measures
dc.subject.keywordKappa Index
dc.subject.ucmInvestigación operativa (Matemáticas)
dc.subject.unesco1207 Investigación Operativa
dc.titleDetermining the accuracy in image supervised classification problemsen
dc.typebook part
dc.volume.number1
dcterms.references[1] S. Haykin, editor. Unsupervised Adaptive Filtering vol. 1: Blind Source Separation, John Willey ans Sons, New York,2000. [2] N. Delfosse and P. Loubaton, Adaptibe blind separation of sources: A deflation approach, Signal Processing, 45:59–83, Elsevier, 1995. [3] S. Cruces, A. Cichocki and S. Amari, The minimum entropy and cumulants based contrast functions for blind source extraction. In J. Mira and A. Prieto, editors, proceedings of the 6th international workshop on artificial neural networks (IWANN 2001), Lecture Notes in Computer Science 2085, pages 786-793, SpringerVerlag,2001. [4] F. Vrins, C. Archambeau and M. Verleysen, Towards a local separation performances estimator using common ICA contrast functions? In M. Verleysen, editor, proceedings of the 12th European Symposium on Artificial Neural Networks (ESANN 2004), d-side pub., pages 211–216, April 28–30, Bruges (Belgium), 2004. [5] A. Amo, D. Gomez, J. Montero and G. Biging, Relevance and redundancy in fuzzy classification systems, Mathware and Soft Computing, 8: 203–216, 2001. [6] A. Amo, J. Montero, G. Biging and V. Cutello, Fuzzy classification systems. European Journal of Operational 156: 459–507, Elsevier, 2004. [7] R.G. Congalton and G. Biging, A pilot study evaluating ground reference data collection efforts for use in forestry inventory. Photogrametic Engineering and Remote Sensing 58: 1669–1671, 1992. [8] E. Binaghi, P.A. Brivio, P. Ghezzi, and A. Rampini, A fuzzy set based accuracy assessment of soft classification. Pattern Recognition Letters,20: 935–948, Elsevier, 1999. 9] R.G. Congalton, A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37: 35–46, Elsevier,1991. [10] R.G. Congalton, and K. Green, editor. Assessing the accuracy of remotely sensed data: Principles and practices. London New York and Washinton D.C: Lewis publishers, 1999 [11] J. Cohen, A coeficient of agreement for nominal scales. Educational and Psychological Measurement, 20: 37–46, 1960. [12] J. Cohen, Weighted Kappa: Nominal Scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin,70: 213–220, 1968. [13] G.M. Foody, Cross-entropy for the evaluation of the accuracy of a fuzzy land cover classification with fuzzy ground data. ISPRS Journal of Photogrammetry and Remote Sensing, 80: 185–201, 1995. [14] G.M. Foody, The continuum of classification fuzziness in thematics mapping. Photogrammetric Engineering and Remote Sensing, 65: 443–451, 1999. [15] G.M. Foody, Status of land cover classification accuracy assessment. Remote Sensing of Environment,80: 185–201, 2002. [16] S. Gopal and C.E. Woodcock, Theory and methods for accuracy assessment of thematic maps using fuzzy sets. Photogrammetric Engineering and Remote Sensing, 60(2): 181–188,1994. [17] D. Gómez, G. Biging and J. Montero, Accuracy statistics for judging soft classification. International Journal of Remote Sensing, 29(3):693–709, 2008. [18] D. Gómez, J. Montero and Biging G., Accuracy measures for fuzzy classiffication in remote sensing. B. Bouchon-Meunier and R.R. Yager, eds. (Editions E.D.K., Paris), 1556–1563, 2006. [19] Green, K. and Congalton, G. (2004). An error matrix approach to fuzzy accuracy assessment: The NIMA geocover project. In R. S. Lunetta and J.G. Lyon (Eds.), Remote sensing and GIS accurcacy assessment (pp. 163-172). Boca Raton: CRC Press. [20] Hagen-Zanker A. (2006). Map comparison methods that simultaneously address overlap and structure. Journal of Geographical Syste
dspace.entity.typePublication
relation.isAuthorOfPublication4dcf8c54-8545-4232-8acf-c163330fd0fe
relation.isAuthorOfPublication9e4cf7df-686c-452d-a98e-7b2602e9e0ea
relation.isAuthorOfPublication.latestForDiscovery9e4cf7df-686c-452d-a98e-7b2602e9e0ea

Download

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Montero111.pdf
Size:
1.08 MB
Format:
Adobe Portable Document Format