Please use this identifier to cite or link to this item: http://archives.univ-biskra.dz/handle/123456789/3138
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dc.contributor.authorBenafia Ali-
dc.contributor.authorZaidi Sahnoun-
dc.contributor.authorBabahenini Med Chaouki-
dc.date.accessioned2013-05-27T21:12:11Z-
dc.date.available2013-05-27T21:12:11Z-
dc.date.issued2013-05-27-
dc.identifier.urihttp://archives.univ-biskra.dz/handle/123456789/3138-
dc.description.abstractIn this paper, we propose a new hybrid approach for indexing images and retrieving images from a corpus of images .The proposed approach is based on the fusion of the low level visual and textual descriptors. First, we describe each image of corpus in a model based on graphs. Then, in a second model, we translate the legends associated to images. These two models permit us to calculate the set of visual and textual descriptors for global index .We uses heuristics for the calculation of these features. The proposed approach is tested on a set of images and the results are compared with the human expert indexing. Link http://www.ijitcs.com/volume%204_No_2/Ali+Benafiabanta.phpen_US
dc.language.isoenen_US
dc.subjectComplex term extraction; linguistic analysis; syntactic patterns; textual features ; visual features.en_US
dc.titleCombining visual and textual features for indexing images: a hybrid approachen_US
dc.typeArticleen_US
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