1. A computer implemented recognition process of an object in a query image, comprising: performing a training step that comprises: merging a plurality of background images with an object tag to provide a set of training images, wherein the plurality of background images comprises real images received from a user or/and artificial images like website images downloaded by an interconnected computer network, and wherein each training image comprising a plurality of pixels; determining for each training image of said 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 selecting among the first descriptors a group of exemplar descriptors describing said set of training images, wherein selecting the exemplary descriptors comprises determining the first descriptors having a number of repetitions in the set of training images higher than a certain value; the method further comprises performing a query step, the query step comprising: receiving the query image and defining a plurality of second descriptors, the second descriptors being vectors describing pixel properties in corresponding subregions of the query image; determining visual similarity coefficients based on a comparison between each one of said second descriptors of the query image and each one of said exemplar descriptors; determining whether second descriptors of the query image match corresponding exemplar descriptors based on the determined visual similarity coefficients; and recognizing the object in the query image based on the determined matches.
12. The process according to claim 1 , wherein each subregion is a circle area of pixels and said first descriptors and/or said second descriptors comprise a distribution of pixels properties in said circle area.