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M. Sharif ; I. Irum ; S. Mohsin ;Journal of Applied Research and Technology 2014, 12 (5)

Autor: M. Yasmin

Fuente: http://www.redalyc.org/


Introducción



Journal of Applied Research and Technology ISSN: 1665-6423 jart@aleph.cinstrum.unam.mx Centro de Ciencias Aplicadas y Desarrollo Tecnológico México Yasmin, M.; Sharif, M.; Irum, I.; Mohsin, S. An Efficient Content Based Image Retrieval using EI Classification and Color Features Journal of Applied Research and Technology, vol.
12, núm.
5, octubre, 2014, pp.
877-885 Centro de Ciencias Aplicadas y Desarrollo Tecnológico Distrito Federal, México Available in: http:--www.redalyc.org-articulo.oa?id=47432518006 How to cite Complete issue More information about this article Journals homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative   An Efficient Content Based Image Retrieval using EI Classification and Color Features M.
Yasmin1, M.
Sharif*2, I.
Irum3 and S.
Mohsin4 1,2,3,4 Department of Computer Science COMSATS Institute of Information Technology, Pakistan *muhammadsharifmalik@yahoo.com 2 Department of Computer Science COMSATS Institute of Information Technology, Pakistan Pakistan ABSTRACT An efficient method for image search and retrieval has been proposed in this study.
For this purpose images are decomposed in equal squares of minimum 24x16 size and then edge detection is applied to those decomposed parts. Pixels classification is done on the basis of edge pixels and inner pixels.
Features are selected from edge pixels for populating the database.
Moreover, color differences are used to cluster same color retrieved results.
Precision and recall rates have been used as quantification measures.
It can be seen from the results that proposed method shows a very good balance of precision and recall in minimum retrieval time, achieved results are comprised of 66%-100% rate for precision and 68%-80% for recall. Keywords: Color Distances, Edge Pixels, Feature Extraction, Image Clustering, Inner Pixels. ...





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