Patent Document ID: 10068154
Application ID: 14907904

Base Claim:
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.

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Claim 13:
13. The process according to claim 1 , wherein said selecting of the exemplar descriptors comprises: providing a feature of the object tag; determining in each training image of said set distances between each of said plurality of first descriptors and the provided feature; determining a class resulting from a selection of the first descriptors of said set of training images having associated thereto distances lower than a threshold; defining class indicator indexes representative of said first descriptors comprised in said class and of said set of training images; generating a quantifying index for each first descriptor of said class based on an elaboration of said class indicator indexes and on the number of said training images comprised in said class, said quantifying index being indicative of repetitions of the associated first descriptor in the set of training images; and selecting a first descriptor if the associated quantifying index is higher than a predefined first index.