Abstract: A computer implemented recognition process of an object in a query image provides a set of training images, each training image being defined by a plurality of pixels and comprising an object tag; determines for each training image of the set a plurality of first descriptors, each first descriptor being a vector that represents pixel properties in a corresponding subregion of the associated training image; and selects among the first descriptors a group of exemplar descriptors describing the set of training images, wherein selecting the exemplar descriptors includes determining the first descriptors having a number of repetitions in the set of training images higher than a certain value.

Abstract: A computer implemented recognition process of an object in a query image provides a set of training images, each training image being defined by a plurality of pixels and comprising an object tag; determines for each training image of the set a plurality of first descriptors, each first descriptor being a vector that represents pixel properties in a corresponding subregion of the associated training image; and selects among the first descriptors a group of exemplar descriptors describing the set of training images, wherein selecting the exemplar descriptors includes determining the first descriptors having a number of repetitions in the set of training images higher than a certain value.

Abstract: Recognition process (1) of an object (An) in a query image (2), performing a training step (6) that comprises: —providing (20) a set of training images (Ti), each training image (Ti) comprising an object tag (LOGOi); —determining (21) for each training image (Ti) of said set a plurality of first descriptors (11), each first descriptor (11) being a vector that represents pixel properties in a subregion (Sri) of the associated training image (Ti); —determining (23) a group of exemplar descriptors (111) describing the set of training images (Ti) and resulting from a selection of the first descriptors (11) based on the position of said subregion (Sri) in the associated training image (Ti) and on the pixel properties of said first descriptors (11); performing a query step (7) comprising: —receiving (30) the query image (2) and defining (31) a plurality of vectors (V?) of second descriptors (3) describing the properties of said query image (2); —determining (35) a visual similarity coefficient based on a comparison

Abstract: Recognition process (1) of an object (An) in a query image (2), performing a training step (6) that comprises: —providing (20) a set of training images (Ti), each training image (Ti) comprising an object tag (LOGOi);—determining (21) for each training image (Ti) of said set a plurality of first descriptors (11), each first descriptor (11) being a vector that represents pixel properties in a subregion (Sri) of the associated training image (Ti);—determining (23) a group of exemplar descriptors (111) describing the set of training images (Ti) and resulting from a selection of the first descriptors (11) based on the position of said subregion (Sri) in the associated training image (Ti) and on the pixel properties of said first descriptors (11); performing a query step (7) comprising:—receiving (30) the query image (2) and defining (31) a plurality of vectors (V?) of second descriptors (3) describing the properties of said query image (2);—determining (35) a visual similarity coefficient based on a comparison bet